diff --git a/.github/workflows/pypi-publish.yml b/.github/workflows/pypi-publish.yml index 834e7e9b..85ad1c9f 100644 --- a/.github/workflows/pypi-publish.yml +++ b/.github/workflows/pypi-publish.yml @@ -12,17 +12,17 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout repo - uses: actions/checkout@v4 + uses: actions/checkout@v6 with: fetch-depth: 0 - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: "3.10" - name: Install uv - uses: astral-sh/setup-uv@v5 + uses: astral-sh/setup-uv@v7 # set version (e.g. 1.2.3) from the latest Git tag on the master branch - name: Set package release version diff --git a/.github/workflows/test-pypi-publish.yml b/.github/workflows/test-pypi-publish.yml index e891da6e..9985786a 100644 --- a/.github/workflows/test-pypi-publish.yml +++ b/.github/workflows/test-pypi-publish.yml @@ -22,17 +22,17 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout repo - uses: actions/checkout@v4 + uses: actions/checkout@v6 with: fetch-depth: 0 - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: "3.10" - name: Install uv - uses: astral-sh/setup-uv@v5 + uses: astral-sh/setup-uv@v7 # set version (e.g. 1.2.3) from the latest Git tag on master branch - name: Set package release version diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 696c7a20..374952e1 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -14,29 +14,28 @@ jobs: strategy: fail-fast: false matrix: - # specific version in 3.13 due to bug https://github.com/python/cpython/issues/138031 - python-version: ["3.10", "3.11", "3.12", "3.13.6"] + python-version: ["3.10", "3.11", "3.12", "3.13"] os: [macos-latest, ubuntu-latest, windows-latest] runs-on: ${{ matrix.os }} steps: - name: Checkout repo - uses: actions/checkout@v4 + uses: actions/checkout@v6 - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} - name: Install uv - uses: astral-sh/setup-uv@v5 + uses: astral-sh/setup-uv@v7 - - uses: actions/cache@v4 + - uses: actions/cache@v5 name: Cache venv with: path: ./.venv key: ${{ matrix.os }}-venv-${{ matrix.python-version }}-${{ hashFiles('**/uv.lock') }} - - uses: actions/cache@v4 + - uses: actions/cache@v5 name: Cache datasets with: path: ~/scikit_learn_data diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 39c33e94..06fda254 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,22 +9,9 @@ repos: language: system pass_filenames: false - - repo: https://github.com/pypa/pip-audit - rev: v2.9.0 - hooks: - - id: pip-audit - args: [ - --vulnerability-service, "pypi", - --cache-dir, ".pip_audit_cache", - # false alert for setuptools, we have a much newer version - --ignore-vuln, "GHSA-5rjg-fvgr-3xxf", - # false alert for pip - --ignore-vuln, "GHSA-4xh5-x5gv-qwph" - ] - - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.13.1 hooks: - id: ruff-check # linter - args: [ --fix ] + args: [ --fix, --exit-zero ] - id: ruff-format # formatter diff --git a/README.md b/README.md index 3f5b7894..54bff714 100644 --- a/README.md +++ b/README.md @@ -53,12 +53,14 @@ Main features: | | `python3.10` | `python3.11` | `python3.12` | `python3.13` | |:-----------:|:------------:|:------------:|:------------:|:------------:| -| **Linux** | ✅ | ✅ | ✅ | ✅ | -| **Windows** | ✅ | ✅ | ✅ | ✅ | -| **macOS** | ✅ | ✅ | ✅ | ✅ | +| **Linux** | ✅ | ✅ | ✅ | ✅ | +| **Windows** | ✅ | ✅ | ✅ | ✅ | +| **macOS** | ✅ | ✅ | ✅ | ✅ | Python 3.9 was supported up to scikit-fingerprints 1.13.0. +Python 3.13 is officially supported, but underlying libraries may not be fully compatible yet. + ## Installation You can install the library using pip: @@ -159,7 +161,7 @@ Examples and tutorials: ## Project overview -`scikit-fingerprint` brings molecular fingerprints and related functionalities into +`scikit-fingerprints` brings molecular fingerprints and related functionalities into the scikit-learn ecosystem. With familiar class-based design and `.transform()` method, fingerprints can be computed from SMILES strings or RDKit `Mol` objects. Resulting NumPy arrays or SciPy sparse arrays can be directly used in ML pipelines. @@ -216,13 +218,16 @@ Publications using scikit-fingerprints: 1. [J. Adamczyk, W. Czech "Molecular Topological Profile (MOLTOP) - Simple and Strong Baseline for Molecular Graph Classification" ECAI 2024](https://ebooks.iospress.nl/doi/10.3233/FAIA240663) 2. [J. Adamczyk, P. Ludynia "Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python" SoftwareX](https://www.sciencedirect.com/science/article/pii/S2352711024003145) 3. [J. Adamczyk, P. Ludynia, W. Czech "Molecular Fingerprints Are Strong Models for Peptide Function Prediction" ArXiv preprint](https://arxiv.org/abs/2501.17901) -4. [M. Fitzner et al. "BayBE: a Bayesian Back End for experimental planning in the low-to-no-data regime" RSC Digital Discovery](https://pubs.rsc.org/en/content/articlehtml/2025/dd/d5dd00050e) -5. [J. Xiong "Bridging 3D Molecular Structures and Artificial Intelligence by a Conformation Description Language"](https://www.biorxiv.org/content/10.1101/2025.05.07.652440v1.abstract) +4. [J. Adamczyk "Towards Rational Pesticide Design with Graph Machine Learning Models for Ecotoxicology" CIKM 2025](https://dl.acm.org/doi/abs/10.1145/3746252.3761660) +5. [J. Adamczyk, J. Poziemski, F. Job, M. Król, M. Makowski "MolPILE - large-scale, diverse dataset for molecular representation learning" ArXiv preprint](https://arxiv.org/abs/2509.18353) +6. [M. Fitzner et al. "BayBE: a Bayesian Back End for experimental planning in the low-to-no-data regime" RSC Digital Discovery](https://pubs.rsc.org/en/content/articlehtml/2025/dd/d5dd00050e) +7. [J. Xiong et al. "Bridging 3D Molecular Structures and Artificial Intelligence by a Conformation Description Language"](https://www.biorxiv.org/content/10.1101/2025.05.07.652440v1.abstract) +8. [S. Mavlonazarova et al. "Untargeted Metabolomics Reveals Organ-Specific and Extraction-Dependent Metabolite Profiles in Endemic Tajik Species Ferula violacea Korovin" bioRxiv preprint](https://www.biorxiv.org/content/10.1101/2025.08.24.671964v1) ## Contributing Please read [CONTRIBUTING.md](CONTRIBUTING.md) and [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) for details on our code of -conduct, and the process for submitting pull requests to us. + conduct and the process for submitting pull requests. ## License diff --git a/mypy.ini b/mypy.ini deleted file mode 100644 index bf1b4bd8..00000000 --- a/mypy.ini +++ /dev/null @@ -1,15 +0,0 @@ -[mypy] -# minimal supported Python version -python_version = 3.10 - -# check all functions, this fixes some tests -check_untyped_defs = true - -# allow redefining variable types, we do that a lot for efficiency -allow_redefinition = true - -# unfortunately, most libraries that we use are not properly typed -# in particular, RDKit is unlikely to ever have proper Python typing stubs -ignore_missing_imports = true -disable_error_code = import-untyped -no_site_packages = true diff --git a/pyproject.toml b/pyproject.toml index 8948cdf5..e888d7cd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,7 @@ dependencies = [ "numba<1", "numpy>=1.20.0,<3", "pandas<3", - "rdkit<=2025.3.6", + "rdkit<=2025.9.3", "scikit-learn>=1.0.0,<2", "scipy>=1.0.0,<2", "tqdm>=4.0.0,<5" @@ -49,21 +49,18 @@ dev = [ "coverage", "jupyter", "mypy", - "pip-audit", "pre-commit", "pytest", "pytest-cov", "pytest-rerunfailures", "ruff", "setuptools>=80", - "xenon" + "scipy-stubs", ] test = [ "mypy", "ruff", - "xenon", - "pip-audit", "pre-commit", "pytest", "pytest-rerunfailures" @@ -73,7 +70,7 @@ docs = [ "ipython", "nbsphinx", "pydata-sphinx-theme", - "scikit-learn!=1.7.1", # due to scikit-learn docs issue: https://github.com/microsoft/lightgbm/issues/6978 + "scikit-learn!=1.7.1", # due to scikit-learn docs issue: https://github.com/microsoft/lightgbm/issues/6978 "sphinx", "sphinx-copybutton" ] @@ -95,6 +92,15 @@ filterwarnings = [ "ignore:Function auroc_score.*:FutureWarning" ] +[tool.mypy] +python_version = "3.10" +check_untyped_defs = true # check all functions, this fixes some tests +allow_redefinition = true # we redefine variables a lot for efficiency +# most libraries used are not properly typed in Python, particularly RDKit +ignore_missing_imports = true +disable_error_code = ["import-untyped"] +no_site_packages = true + [tool.uv.build-backend] module-name = "skfp" module-root = "" diff --git a/skfp/applicability_domain/bounding_box.py b/skfp/applicability_domain/bounding_box.py index 55e9b20c..9b07ef43 100644 --- a/skfp/applicability_domain/bounding_box.py +++ b/skfp/applicability_domain/bounding_box.py @@ -15,10 +15,10 @@ class BoundingBoxADChecker(BaseADChecker): This creates a "bounding box" using their extreme values, and new molecules should lie in this distribution, i.e. have properties in the same ranges [1]_. - Typically, physicochemical properties (continous features) are used as inputs. + Typically, physicochemical properties (continuous features) are used as inputs. Consider scaling, normalizing, or transforming them before computing AD to lessen effects of outliers, e.g. with ``PowerTransformer`` or ``RobustScaler``. This is - particularly important if ``"three_sigma"`` is used as percentile bound, as it + particularly important if ``"three_sigma"`` is used as the percentile bound, as it assumes normal distribution. By default, the full range of training descriptors are allowed as AD. For stricter @@ -26,7 +26,7 @@ class BoundingBoxADChecker(BaseADChecker): extremely low or large values, respectively. For looser check, use ``num_allowed_violations`` to allow a number of desrciptors to lie outside the given ranges. - This method scales very well with both number of samples and features. + This method scales very well with both the number of samples and features. Parameters ---------- @@ -42,7 +42,7 @@ class BoundingBoxADChecker(BaseADChecker): uses 3 standard deviations from the mean, a common rule-of-thumb for outliers assuming the normal distribution. - num_allowed_violations : bool, default=0 + num_allowed_violations : int, default=0 Number of allowed violations of feature ranges. By default, all descriptors must lie inside the bounding box. @@ -85,8 +85,8 @@ class BoundingBoxADChecker(BaseADChecker): _parameter_constraints: dict = { **BaseADChecker._parameter_constraints, - "percentile_lower": [Interval(Real, 0, 100, closed="both")], - "percentile_upper": [Interval(Real, 0, 100, closed="both")], + "percentile_lower": [Interval(Real, 0, 100, closed="both"), "three_sigma"], + "percentile_upper": [Interval(Real, 0, 100, closed="both"), "three_sigma"], "num_allowed_violations": [Interval(Integral, 0, None, closed="left")], } @@ -94,7 +94,7 @@ def __init__( self, percentile_lower: float | str = 0, percentile_upper: float | str = 100, - num_allowed_violations: int | None = 0, + num_allowed_violations: int = 0, n_jobs: int | None = None, verbose: int | dict = 0, ): diff --git a/skfp/applicability_domain/convex_hull.py b/skfp/applicability_domain/convex_hull.py index b742c832..46035fc0 100644 --- a/skfp/applicability_domain/convex_hull.py +++ b/skfp/applicability_domain/convex_hull.py @@ -35,11 +35,11 @@ class ConvexHullADChecker(BaseADChecker): & 1^T \lambda = 1,\\ & \lambda_i \geq 0 \text{ for all } i=1,...,n - Typically, physicochemical properties (continous features) are used as inputs. + Typically, physicochemical properties (continuous features) are used as inputs. Consider scaling, normalizing, or transforming them before computing AD to lessen effects of outliers, e.g. with ``PowerTransformer`` or ``RobustScaler``. - This method scales very badly with both number of samples and features. It has + This method scales very badly with both the number of samples and features. It has quadratic scaling :math:`O(n^2)` in number of samples, and can be realistically run on at most 1000-3000 molecules. Its geometry also breaks down above ~10 features, marking everything as outside AD. diff --git a/skfp/applicability_domain/distance_to_centroid.py b/skfp/applicability_domain/distance_to_centroid.py index 6b8c26e7..a81ab02e 100644 --- a/skfp/applicability_domain/distance_to_centroid.py +++ b/skfp/applicability_domain/distance_to_centroid.py @@ -63,7 +63,7 @@ class DistanceToCentroidADChecker(BaseADChecker): data centroid, i.e. the average (middle) point [1]_. New molecules should lie inside the hypersphere of a given radius (distance) from that centroid. - Typically, physicochemical properties (continous features) are used as inputs. + Typically, physicochemical properties (continuous features) are used as inputs. Consider scaling, normalizing, or transforming them before computing AD to lessen effects of outliers, e.g. with ``PowerTransformer`` or ``RobustScaler``. @@ -129,7 +129,7 @@ class DistanceToCentroidADChecker(BaseADChecker): _parameter_constraints: dict = { **BaseADChecker._parameter_constraints, "threshold": [Interval(Real, 0, None, closed="neither"), StrOptions({"auto"})], - "distance": [ + "metric": [ callable, StrOptions(SCIPY_METRIC_NAMES | SKFP_METRIC_NAMES | SKFP_BULK_METRIC_NAMES), ], diff --git a/skfp/applicability_domain/hotelling_t2_test.py b/skfp/applicability_domain/hotelling_t2_test.py index 53cb5eb5..802e296a 100644 --- a/skfp/applicability_domain/hotelling_t2_test.py +++ b/skfp/applicability_domain/hotelling_t2_test.py @@ -16,7 +16,7 @@ class HotellingT2TestADChecker(BaseADChecker): Mahalanobis distance of a new sample from the mean of the training data, scaled by the covariance structure of the training data. - Typically, physicochemical properties (continous features) are used as inputs. + Typically, physicochemical properties (continuous features) are used as inputs. Consider scaling, normalizing, or transforming them before computing AD to lessen effects of outliers, e.g. with ``PowerTransformer`` or ``RobustScaler``. In case of Hotelling's T^2 test, using PCA beforehand to obtain orthogonal features is diff --git a/skfp/applicability_domain/knn.py b/skfp/applicability_domain/knn.py index 7b26c78c..48c4d9ad 100644 --- a/skfp/applicability_domain/knn.py +++ b/skfp/applicability_domain/knn.py @@ -54,10 +54,15 @@ class KNNADChecker(BaseADChecker): Distance metric to use. agg: "mean" or "max" or "min", default="mean" - Aggregation method for distances to k nearest neigbors: - - "mean": use the mean distance to k neighbors, - - "max": use the maximum distance among k neighbors, - - "min": use the distance to the closest neigbor. + Aggregation method for distances to k nearest neighbors: + + - "mean": average distance + - "max": maximum distance, to k-th neighbor + - "min": minimal distance, to the nearest neighbor + + threshold : float, default=95 + Percentile of distance distribution, used as the threshold for determining the + applicability domain. Value in range ``[0, 100]``. n_jobs : int, default=None The number of jobs to run in parallel. :meth:`transform_x_y` and @@ -99,11 +104,8 @@ class KNNADChecker(BaseADChecker): ... ]) >>> X_test_binary = 1 - X_train_binary >>> knn_ad_checker_binary = KNNADChecker(k=2, metric="tanimoto_binary_distance", agg="mean") - >>> knn_ad_checker_binary - KNNADChecker() - >>> knn_ad_checker_binary.fit(X_train_binary) - KNNADChecker() + KNNADChecker(k=2) >>> knn_ad_checker_binary.predict(X_test_binary) array([False, False, False]) @@ -115,7 +117,7 @@ class KNNADChecker(BaseADChecker): "k": [Interval(Integral, 1, None, closed="left")], "metric": [callable, StrOptions(METRIC_NAMES)], "agg": [StrOptions({"mean", "max", "min"})], - "threshold": [None, Interval(Real, 0, 1, closed="both")], + "threshold": [None, Interval(Real, 0, 100, closed="both")], } def __init__( @@ -123,7 +125,7 @@ def __init__( k: int = 1, metric: str | Callable = "tanimoto_binary_distance", agg: str = "mean", - threshold: float = 0.95, + threshold: float = 95, n_jobs: int | None = None, verbose: int | dict = 0, ): @@ -155,7 +157,12 @@ def fit( # noqa: D102 ) self.X_train_ = X - self.k_used = 1 if self.agg == "min" else self.k + + # k+1, since we need to exclude each point from being its own neighbor + if self.agg == "min": + self._k_used = min(len(X), 2) + else: + self._k_used = min(len(X), self.k + 1) if callable(self.metric): metric_func = self.metric @@ -167,14 +174,19 @@ def fit( # noqa: D102 ) self.knn_ = NearestNeighbors( - n_neighbors=self.k_used, metric=metric_func, n_jobs=self.n_jobs + n_neighbors=self._k_used, metric=metric_func, n_jobs=self.n_jobs ) self.knn_.fit(X) k_nearest, _ = self.knn_.kneighbors(X) + # exclude the point itself from the neighbors + k_nearest = k_nearest[:, 1:] + agg_dists = self._get_agg_dists(k_nearest) self.threshold_ = np.percentile(agg_dists, self.threshold) + return self + def predict(self, X: np.ndarray) -> np.ndarray: # noqa: D102 return self.score_samples(X) <= self.threshold_ @@ -195,11 +207,10 @@ def score_samples(self, X: np.ndarray) -> np.ndarray: """ check_is_fitted(self) X = validate_data(self, X=X, reset=False) - k_nearest, _ = self.knn_.kneighbors(X, n_neighbors=self.k_used) - + k_nearest, _ = self.knn_.kneighbors(X, n_neighbors=self._k_used) return self._get_agg_dists(k_nearest) - def _get_agg_dists(self, k_nearest) -> np.ndarray: + def _get_agg_dists(self, k_nearest: np.ndarray) -> np.ndarray: if self.agg == "mean": agg_dists = np.mean(k_nearest, axis=1) elif self.agg == "max": diff --git a/skfp/applicability_domain/leverage.py b/skfp/applicability_domain/leverage.py index 16013e1d..2383b46b 100644 --- a/skfp/applicability_domain/leverage.py +++ b/skfp/applicability_domain/leverage.py @@ -26,7 +26,7 @@ class LeverageADChecker(BaseADChecker): training data is used as an outlier score. Typical threshold is :math:`3(d+1)/n`, where `d` is the number of features and `n` is the number of training molecules. - Typically, physicochemical properties (continous features) are used as inputs. + Typically, physicochemical properties (continuous features) are used as inputs. Consider scaling, normalizing, or transforming them before computing AD to lessen effects of outliers, e.g. with ``PowerTransformer`` or ``RobustScaler``. Features should not be too strongly correlated, as this can result in near-singular matrix diff --git a/skfp/applicability_domain/pca_bounding_box.py b/skfp/applicability_domain/pca_bounding_box.py index e9a1da14..7e0cd38c 100644 --- a/skfp/applicability_domain/pca_bounding_box.py +++ b/skfp/applicability_domain/pca_bounding_box.py @@ -17,11 +17,11 @@ class PCABoundingBoxADChecker(BaseADChecker): Analysis (PCA) [1]_. AD is a hyperrectangle, with thresholds defined as minimal and maximal values from the training set on PCA axes. - Typically, physicochemical properties (continous features) are used as inputs. + Typically, physicochemical properties (continuous features) are used as inputs. Consider scaling, normalizing, or transforming them before computing AD to lessen effects of outliers, e.g. with ``PowerTransformer`` or ``RobustScaler``. - This method scales very well with both number of samples and features, but doesn't + This method scales very well with both the number of samples and features, but doesn't work well for highly sparse input features (e.g. many molecular fingerprints), since PCA centers the data. diff --git a/skfp/applicability_domain/prob_std.py b/skfp/applicability_domain/prob_std.py index 5e32ae73..4b1b266b 100644 --- a/skfp/applicability_domain/prob_std.py +++ b/skfp/applicability_domain/prob_std.py @@ -73,10 +73,10 @@ class ProbStdADChecker(BaseADChecker): >>> y_train = (X_train[:, 0] + X_train[:, 1] > 1).astype(float) >>> model = RandomForestRegressor(n_estimators=10, random_state=0) >>> model.fit(X_train, y_train) + RandomForestRegressor(n_estimators=10, random_state=0) >>> probstd_ad_checker = ProbStdADChecker(model=model, threshold=0.1) >>> probstd_ad_checker.fit() - >>> probstd_ad_checker - ProbStdADChecker(model=RandomForestRegressor(...), threshold=0.1) + ProbStdADChecker(model=RandomForestRegressor(n_estimators=10, random_state=0)) >>> X_test = np.random.uniform(0, 1, size=(100, 5)) >>> probstd_ad_checker.predict(X_test).shape diff --git a/skfp/applicability_domain/standard_deviation.py b/skfp/applicability_domain/standard_deviation.py index 23ffe393..6cee5d53 100644 --- a/skfp/applicability_domain/standard_deviation.py +++ b/skfp/applicability_domain/standard_deviation.py @@ -69,10 +69,10 @@ class StandardDeviationADChecker(BaseADChecker): >>> y_train = X_train.sum(axis=1) >>> model = RandomForestRegressor(n_estimators=5, random_state=0) >>> model.fit(X_train, y_train) + RandomForestRegressor(n_estimators=5, random_state=0) >>> std_ad_checker = StandardDeviationADChecker(model=model, threshold=0.5) - >>> std_ad_checker.fit() - >>> std_ad_checker - StandardDeviationADChecker() + >>> std_ad_checker.fit() # doctest: +NORMALIZE_WHITESPACE + StandardDeviationADChecker(model=RandomForestRegressor(n_estimators=5, random_state=0), threshold=0.5) >>> X_test = np.random.uniform(0, 1, size=(100, 5)) >>> std_ad_checker.predict(X_test).shape diff --git a/skfp/applicability_domain/topkat.py b/skfp/applicability_domain/topkat.py index 88c409a9..18dacb3d 100644 --- a/skfp/applicability_domain/topkat.py +++ b/skfp/applicability_domain/topkat.py @@ -62,7 +62,7 @@ class TOPKATADChecker(BaseADChecker): TOPKATADChecker() >>> topkat_ad_checker.predict(X_test) - array([ True, True, True, True, True]) + array([ True, True, True, True, False]) """ _parameter_constraints: dict = { diff --git a/skfp/bases/base_ad_checker.py b/skfp/bases/base_ad_checker.py index a3ce16db..c025dc63 100644 --- a/skfp/bases/base_ad_checker.py +++ b/skfp/bases/base_ad_checker.py @@ -89,7 +89,7 @@ def fit(self, X: np.ndarray, y: np.ndarray | None = None): @abstractmethod def predict(self, X: np.ndarray) -> np.ndarray: """ - Predict labels (1 inside AD, 0 outside AD) of X according to fitted model. + Predict labels (1 inside AD, 0 outside AD) of X according to the fitted model. Parameters ---------- @@ -99,7 +99,7 @@ def predict(self, X: np.ndarray) -> np.ndarray: Returns ------- is_inside_applicability_domain : ndarray of shape (n_samples,) - Returns 1 for molecules inside applicability domain, and 0 for those + Returns 1 for molecules inside the applicability domain, and 0 for those outside (outliers). """ raise NotImplementedError diff --git a/skfp/datasets/moleculeace/moleculeace.py b/skfp/datasets/moleculeace/moleculeace.py index 92bcdbf4..abd31277 100644 --- a/skfp/datasets/moleculeace/moleculeace.py +++ b/skfp/datasets/moleculeace/moleculeace.py @@ -25,7 +25,6 @@ def load_chembl204_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Prothrombin target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -69,7 +68,7 @@ def load_chembl204_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl204_ki + >>> from skfp.datasets.moleculeace import load_chembl204_ki >>> dataset = load_chembl204_ki() >>> dataset # doctest: +SKIP (['CC(=N)N1CCC(Oc2ccc3nc(CCC(=O)O)n(Cc4ccc5ccc(C(=N)N)cc5c4)c3c2)CC1, ..., 'CCC(=O)N1CCC[C@H]1C(=O)NCc1ccc(C(=N)N)cc1'], \ @@ -77,12 +76,12 @@ def load_chembl204_ki( >>> dataset = load_chembl204_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 CC(=N)N1CCC(Oc2ccc3nc(CCC(=O)O)n(Cc4ccc5ccc(C(=N)N)cc5c4)c3c2)CC1 -3.426511 - 1 CC(=N)N1CCC(Oc2ccc3c(c2)nc(C(C)C)n3Cc2ccc3ccc(C(=N)N)cc3c2)CC1 -2.939519 - 2 CCC(C)c1nc2cc(OC3CCN(C(C)=N)CC3)ccc2n1Cc1ccc2ccc(C(=N)N)cc2c1 -3.361728 - 3 COC(=O)C(C)CN(c1ccc2c(c1)nc(C)n2Cc1ccc2ccc(C(=N)N)cc2c1)C1CCN(C(C)=N)CC1 -3.698970 - 4 CCCCc1nc2cc(OC3CCN(C(C)=N)CC3)ccc2n1Cc1ccc2ccc(C(=N)N)cc2c1 -3.301030 + SMILES Ki + 0 CC(=N)N1CCC(Oc2ccc3nc(CCC(=O)O)n(Cc4ccc5ccc(C(... -3.426511 + 1 CC(=N)N1CCC(Oc2ccc3c(c2)nc(C(C)C)n3Cc2ccc3ccc(... -2.939519 + 2 CCC(C)c1nc2cc(OC3CCN(C(C)=N)CC3)ccc2n1Cc1ccc2c... -3.361728 + 3 COC(=O)C(C)CN(c1ccc2c(c1)nc(C)n2Cc1ccc2ccc(C(=... -3.698970 + 4 CCCCc1nc2cc(OC3CCN(C(C)=N)CC3)ccc2n1Cc1ccc2ccc... -3.301030 """ df = fetch_dataset( data_dir, @@ -111,7 +110,6 @@ def load_chembl214_ki( The task is to predict the inhibitor constant (Ki) of molecules against the 5-hydroxytryptamine receptor 1a target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -155,7 +153,7 @@ def load_chembl214_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl214_ki + >>> from skfp.datasets.moleculeace import load_chembl214_ki >>> dataset = load_chembl214_ki() >>> dataset # doctest: +SKIP (['COc1ccc(NC(=O)c2ccc(-c3ccc(-c4noc(C)n4)cc3C)cc2)cc1N1CCN(C)CC1, ..., 'O=S(=O)(NCCCCCCN1CCN(c2nsc3ccccc23)CC1)c1ccc2ccccc2c1'], \ @@ -163,12 +161,13 @@ def load_chembl214_ki( >>> dataset = load_chembl214_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 COc1ccc(NC(=O)c2ccc(-c3ccc(-c4noc(C)n4)cc3C)cc2)cc1N1CCN(C)CC1 -1.869232 - 1 Nc1cccc(-c2ccc(CCN3CCN(c4cccc5cccnc45)CC3)cc2)n1 -0.477121 - 2 COc1ccc(NS(=O)(=O)c2ccc(Br)cc2)cc1N1CCN(C)CC1 -2.400002 - 3 COc1ccc(NS(=O)(=O)c2sc3ccc(Cl)cc3c2C)cc1N1CCN(C)CC1 -2.700002 - 4 CN1CCc2cccc3c2[C@H]1Cc1cccc(-c2ccccc2)c1-3 -0.255273 + SMILES Ki + 0 COc1ccc(NC(=O)c2ccc(-c3ccc(-c4noc(C)n4)cc3C)cc... -1.869232 + 1 Nc1cccc(-c2ccc(CCN3CCN(c4cccc5cccnc45)CC3)cc2)n1 -0.477121 + 2 COc1ccc(NS(=O)(=O)c2ccc(Br)cc2)cc1N1CCN(C)CC1 -2.400002 + 3 COc1ccc(NS(=O)(=O)c2sc3ccc(Cl)cc3c2C)cc1N1CCN(... -2.700002 + 4 CN1CCc2cccc3c2[C@H]1Cc1cccc(-c2ccccc2)c1-3 -0.255273 + """ df = fetch_dataset( data_dir, @@ -197,7 +196,6 @@ def load_chembl218_ec50( The task is to predict the half maximal effective concentration (EC50) of molecules against the Cannabinoid receptor 1 target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -241,7 +239,7 @@ def load_chembl218_ec50( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl218_ec50 + >>> from skfp.datasets.moleculeace import load_chembl218_ec50 >>> dataset = load_chembl218_ec50() >>> dataset # doctest: +SKIP (['Cn1c(C(=O)NN2CCCCC2)nc(-c2ccc(Cl)cc2)c1-c1ccc(Cl)cc1, ..., 'CCCCCc1cccc(OCCCCCCCCCCC(=O)NC2CC2)c1'], \ @@ -249,12 +247,12 @@ def load_chembl218_ec50( >>> dataset = load_chembl218_ec50(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES EC50 - 0 Cn1c(C(=O)NN2CCCCC2)nc(-c2ccc(Cl)cc2)c1-c1ccc(Cl)cc1 -2.000000 - 1 Cn1c(C(=O)NC2CCCCC2)nc(-c2ccc(Cl)cc2)c1-c1ccc(Cl)cc1 -2.698970 - 2 Cn1c(C(=O)NN2CCCCC2)nc(-c2ccc(Cl)cc2Cl)c1-c1ccc(Cl)cc1 -0.698970 - 3 Cn1c(C(=O)NC2CCCCC2)nc(-c2ccc(Cl)cc2Cl)c1-c1ccc(Cl)cc1 -1.255273 - 4 N#Cc1cc(-c2ccc(Cl)cc2)c(-c2ccc(Cl)cc2Cl)nc1OCc1ccc(F)c(F)c1 -0.903090 + SMILES EC50 + 0 Cn1c(C(=O)NN2CCCCC2)nc(-c2ccc(Cl)cc2)c1-c1ccc(... -2.000000 + 1 Cn1c(C(=O)NC2CCCCC2)nc(-c2ccc(Cl)cc2)c1-c1ccc(... -2.698970 + 2 Cn1c(C(=O)NN2CCCCC2)nc(-c2ccc(Cl)cc2Cl)c1-c1cc... -0.698970 + 3 Cn1c(C(=O)NC2CCCCC2)nc(-c2ccc(Cl)cc2Cl)c1-c1cc... -1.255273 + 4 N#Cc1cc(-c2ccc(Cl)cc2)c(-c2ccc(Cl)cc2Cl)nc1OCc... -0.903090 """ df = fetch_dataset( data_dir, @@ -283,7 +281,6 @@ def load_chembl219_ki( The task is to predict the inhibitor constant (Ki) of molecules against the D(4) dopamine receptor target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -327,7 +324,7 @@ def load_chembl219_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl219_ki + >>> from skfp.datasets.moleculeace import load_chembl219_ki >>> dataset = load_chembl219_ki() >>> dataset # doctest: +SKIP (['COc1ccccc1N1CCN(Cc2ccn(-c3ccccc3)c2)CC1, ..., 'CNc1cc(OC)c(C(=O)N[C@@H]2CCN(Cc3ccccc3)[C@@H]2C)cc1Cl'], \ @@ -335,12 +332,12 @@ def load_chembl219_ki( >>> dataset = load_chembl219_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 COc1ccccc1N1CCN(Cc2ccn(-c3ccccc3)c2)CC1 -0.113943 - 1 c1ccc(N2CCN(Cc3ccn(-c4ccccc4)c3)CC2)cc1 -0.602060 - 2 CC1Cc2cccc3c2N1C(=O)C(N1CCN(Cc2ccc(Cl)cc2)CC1)CC3 -0.954243 - 3 CC1(C)Cc2cccc3c2N1C(=O)C(N1CCN(Cc2ccc(Cl)cc2)CC1)CC3 -1.278754 - 4 Cc1ccc(CN2CCN(C3CCc4cccc5c4N(CC5)C3=O)CC2)cc1 -0.602060 + SMILES Ki + 0 COc1ccccc1N1CCN(Cc2ccn(-c3ccccc3)c2)CC1 -0.113943 + 1 c1ccc(N2CCN(Cc3ccn(-c4ccccc4)c3)CC2)cc1 -0.602060 + 2 CC1Cc2cccc3c2N1C(=O)C(N1CCN(Cc2ccc(Cl)cc2)CC1)CC3 -0.954243 + 3 CC1(C)Cc2cccc3c2N1C(=O)C(N1CCN(Cc2ccc(Cl)cc2)C... -1.278754 + 4 Cc1ccc(CN2CCN(C3CCc4cccc5c4N(CC5)C3=O)CC2)cc1 -0.602060 """ df = fetch_dataset( data_dir, @@ -369,7 +366,6 @@ def load_chembl228_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Sodium-dependent serotonin transporter target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -413,7 +409,7 @@ def load_chembl228_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl228_ki + >>> from skfp.datasets.moleculeace import load_chembl228_ki >>> dataset = load_chembl228_ki() >>> dataset # doctest: +SKIP (['CN(C)Cc1ccccc1Sc1ccc(C#N)cc1N, ..., 'CCCN(CC[C@]1(O)C[C@H](NC(=O)c2ccc3ccccc3c2)C1)[C@H]1CCc2nc(N)sc2C1'], \ @@ -455,7 +451,6 @@ def load_chembl231_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Histamine h1 receptor target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -499,7 +494,7 @@ def load_chembl231_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl231_ki + >>> from skfp.datasets.moleculeace import load_chembl231_ki >>> dataset = load_chembl231_ki() >>> dataset # doctest: +SKIP (['CN1CCN(C2=Nc3ccccc3Nc3sc(CO)cc32)CC1, ..., 'O=C(O)c1cc(-c2ccc(C3CCNCC3)cc2)cc(-n2cc(-c3ccc(Cl)s3)nn2)c1'], \ @@ -507,12 +502,12 @@ def load_chembl231_ki( >>> dataset = load_chembl231_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 CN1CCN(C2=Nc3ccccc3Nc3sc(CO)cc32)CC1 -0.778151 - 1 Cc1cc2c(s1)Nc1ccccc1N=C2N1CCN(C)CC1 -0.622900 - 2 Cc1cc2c(s1)Nc1ccccc1N=C2N1CCNCC1 -1.342423 - 3 Cc1cc2c(s1)Nc1ccccc1N=C2N1CC[N+](C)([O-])CC1 -1.939519 - 4 CC(=O)c1ccc(OCCCN2CC[C@H](NC(=O)[C@@H](N)CO)C2)cc1 -4.633468 + SMILES Ki + 0 CN1CCN(C2=Nc3ccccc3Nc3sc(CO)cc32)CC1 -0.778151 + 1 Cc1cc2c(s1)Nc1ccccc1N=C2N1CCN(C)CC1 -0.622900 + 2 Cc1cc2c(s1)Nc1ccccc1N=C2N1CCNCC1 -1.342423 + 3 Cc1cc2c(s1)Nc1ccccc1N=C2N1CC[N+](C)([O-])CC1 -1.939519 + 4 CC(=O)c1ccc(OCCCN2CC[C@H](NC(=O)[C@@H](N)CO)C2... -4.633468 """ df = fetch_dataset( data_dir, @@ -541,7 +536,6 @@ def load_chembl233_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Mu-type opioid receptor target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -585,7 +579,7 @@ def load_chembl233_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl233_ki + >>> from skfp.datasets.moleculeace import load_chembl233_ki >>> dataset = load_chembl233_ki() >>> dataset # doctest: +SKIP (['CC(c1ccccc1)N1CC[C@H]1[C@@H](N)c1cccc(Cl)c1, ..., 'CCO[C@@]12Cc3cc(-c4ccccc4)cnc3[C@@H]3Oc4c(O)ccc5c4[C@@]31CCN(CC1CC1)[C@@H]2C5'], \ @@ -593,12 +587,12 @@ def load_chembl233_ki( >>> dataset = load_chembl233_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 CC(c1ccccc1)N1CC[C@H]1[C@@H](N)c1cccc(Cl)c1 -4.026125 - 1 Cc1ccc(C(c2ccc(C)cc2)N2CC[C@H]2[C@H](N)c2cccc(Cl)c2)cc1 -2.903633 - 2 COc1ccc([C@H](N)[C@@H]2CCN2C(c2ccccc2)c2ccccc2)cc1 -2.937016 - 3 N[C@H](c1cccc(Cl)c1)[C@@H]1CCN1C(c1ccc(F)cc1)c1ccc(F)cc1 -3.337659 - 4 N[C@H](c1cccc(Cl)c1)[C@@H]1CCN1C(c1cccc(Cl)c1)c1cccc(Cl)c1 -3.854852 + SMILES Ki + 0 CC(c1ccccc1)N1CC[C@H]1[C@@H](N)c1cccc(Cl)c1 -4.026125 + 1 Cc1ccc(C(c2ccc(C)cc2)N2CC[C@H]2[C@H](N)c2cccc(... -2.903633 + 2 COc1ccc([C@H](N)[C@@H]2CCN2C(c2ccccc2)c2ccccc2... -2.937016 + 3 N[C@H](c1cccc(Cl)c1)[C@@H]1CCN1C(c1ccc(F)cc1)c... -3.337659 + 4 N[C@H](c1cccc(Cl)c1)[C@@H]1CCN1C(c1cccc(Cl)c1)... -3.854852 """ df = fetch_dataset( data_dir, @@ -627,7 +621,6 @@ def load_chembl234_ki( The task is to predict the inhibitor constant (Ki) of molecules against the D(3) dopamine receptor target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -671,7 +664,7 @@ def load_chembl234_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl234_ki + >>> from skfp.datasets.moleculeace import load_chembl234_ki >>> dataset = load_chembl234_ki() >>> dataset # doctest: +SKIP (['CN1C2CCC1CC(OC(c1ccc(F)cc1)c1ccc(F)cc1)C2, ..., 'CNc1cc(OC)c(C(=O)N[C@@H]2CCN(Cc3ccccc3)[C@@H]2C)cc1Cl'], \ @@ -679,12 +672,12 @@ def load_chembl234_ki( >>> dataset = load_chembl234_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 CN1C2CCC1CC(OC(c1ccc(F)cc1)c1ccc(F)cc1)C2 -2.161368 - 1 O=C(NCCCN1CCN(c2cccc(Cl)c2Cl)CC1)c1cccc2c1-c1ccccc1C2=O -1.556303 - 2 c1ccc(N2CCN(CCCn3c4ccccc4c4ccccc43)CC2)cc1 -3.383815 - 3 Oc1nc2c(N3CCN(Cc4ccccc4)CC3)cccc2[nH]1 -1.752048 - 4 O=C(NCCCN1CCN(c2ccccc2)CC1)c1cccc2c1-c1ccccc1C2=O -2.633468 + SMILES Ki + 0 CN1C2CCC1CC(OC(c1ccc(F)cc1)c1ccc(F)cc1)C2 -2.161368 + 1 O=C(NCCCN1CCN(c2cccc(Cl)c2Cl)CC1)c1cccc2c1-c1c... -1.556303 + 2 c1ccc(N2CCN(CCCn3c4ccccc4c4ccccc43)CC2)cc1 -3.383815 + 3 Oc1nc2c(N3CCN(Cc4ccccc4)CC3)cccc2[nH]1 -1.752048 + 4 O=C(NCCCN1CCN(c2ccccc2)CC1)c1cccc2c1-c1ccccc1C2=O -2.633468 """ df = fetch_dataset( data_dir, @@ -713,7 +706,6 @@ def load_chembl235_ec50( The task is to predict the half maximal effective concentration (EC50) of molecules against the Peroxisome proliferator-activated receptor gamma target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -757,7 +749,7 @@ def load_chembl235_ec50( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl235_ec50 + >>> from skfp.datasets.moleculeace import load_chembl235_ec50 >>> dataset = load_chembl235_ec50() >>> dataset # doctest: +SKIP (['CC(/C=C/C(F)=C(/C)c1cc(C(C)(C)C)cc(C(C)(C)C)c1OCC(F)(F)F)=C\C(=O)O, ..., 'O=C(O)Cc1cc(Br)c(Oc2cc(I)c(O)c(I)c2)c(I)c1'], \ @@ -765,12 +757,12 @@ def load_chembl235_ec50( >>> dataset = load_chembl235_ec50(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES EC50 - 0 CC(/C=C/C(F)=C(/C)c1cc(C(C)(C)C)cc(C(C)(C)C)c1OCC(F)(F)F)=C\C(=O)O -1.324282 - 1 CCCOc1c(/C(C)=C\C=C\C(C)=C\C(=O)O)cc(C(C)C)cc1C(F)(F)C(F)(F)F -1.343409 - 2 C/C(=C/C=C/C(C)=C/C(=O)O)c1cc(-c2cccs2)cc(C(C)C)c1OCC(F)F -0.993436 - 3 CCC(Cc1ccc(OC)c(C(=O)NCc2ccc(OCCc3ccccc3)cc2)c1)C(=O)O -3.477121 - 4 CCCCC(Cc1ccc(OC)c(C(=O)NCc2ccc(C(F)(F)F)cc2)c1)C(=O)O -3.397940 + SMILES EC50 + 0 CC(/C=C/C(F)=C(/C)c1cc(C(C)(C)C)cc(C(C)(C)C)c1... -1.324282 + 1 CCCOc1c(/C(C)=C\C=C\C(C)=C\C(=O)O)cc(C(C)C)cc1... -1.343409 + 2 C/C(=C/C=C/C(C)=C/C(=O)O)c1cc(-c2cccs2)cc(C(C)... -0.993436 + 3 CCC(Cc1ccc(OC)c(C(=O)NCc2ccc(OCCc3ccccc3)cc2)c... -3.477121 + 4 CCCCC(Cc1ccc(OC)c(C(=O)NCc2ccc(C(F)(F)F)cc2)c1... -3.397940 """ df = fetch_dataset( data_dir, @@ -799,7 +791,6 @@ def load_chembl236_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Delta-type opioid receptor target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -843,7 +834,7 @@ def load_chembl236_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl236_ki + >>> from skfp.datasets.moleculeace import load_chembl236_ki >>> dataset = load_chembl236_ki() >>> dataset # doctest: +SKIP (['CC(c1ccccc1)N1CC[C@H]1[C@@H](N)c1cccc(Cl)c1, ..., 'CCO[C@@]12Cc3cc(-c4ccccc4)cnc3[C@@H]3Oc4c(O)ccc5c4[C@@]31CCN(CC1CC1)[C@@H]2C5'], \ @@ -851,12 +842,12 @@ def load_chembl236_ki( >>> dataset = load_chembl236_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 CC(c1ccccc1)N1CC[C@H]1[C@@H](N)c1cccc(Cl)c1 -4.592399 - 2 Cc1ccc(C(c2ccc(C)cc2)N2CC[C@H]2[C@H](N)c2cccc(Cl)c2)cc1 -3.699924 - 4 COc1ccc([C@H](N)[C@@H]2CCN2C(c2ccccc2)c2ccccc2)cc1 -3.465234 - 5 N[C@H](c1cccc(Cl)c1)[C@@H]1CCN1C(c1ccc(F)cc1)c1ccc(F)cc1 -3.870989 - 6 N[C@H](c1cccc(Cl)c1)[C@@H]1CCN1C(c1cccc(Cl)c1)c1cccc(Cl)c1 -3.432809 + SMILES Ki + 0 CC(c1ccccc1)N1CC[C@H]1[C@@H](N)c1cccc(Cl)c1 -4.592399 + 1 O=C(CCC(=O)NCC(=O)N[C@@H]1CC[C@@]2(O)[C@H]3Cc4... -0.892095 + 2 Cc1ccc(C(c2ccc(C)cc2)N2CC[C@H]2[C@H](N)c2cccc(... -3.699924 + 3 O=C(CCC(=O)NCC(=O)NCC(=O)NCC(=O)N[C@@H]1CC[C@@... -0.806180 + 4 COc1ccc([C@H](N)[C@@H]2CCN2C(c2ccccc2)c2ccccc2... -3.465234 """ df = fetch_dataset( data_dir, @@ -885,7 +876,6 @@ def load_chembl237_ec50( The task is to predict the half maximal effective concentration (EC50) of molecules against the Kappa-type opioid receptor target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -929,7 +919,7 @@ def load_chembl237_ec50( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl237_ec50 + >>> from skfp.datasets.moleculeace import load_chembl237_ec50 >>> dataset = load_chembl237_ec50() >>> dataset # doctest: +SKIP (['C=CCN1CC[C@]23c4c5ccc(O)c4O[C@H]2C(=O)CC[C@@]3(O)[C@H]1C5, ..., 'Oc1ccc2c3c1O[C@H]1c4ncc(-c5ccccc5)cc4C[C@@]4(OCCCC5CCCCC5)[C@@H](C2)N(CC2CC2)CC[C@]314'], \ @@ -937,12 +927,12 @@ def load_chembl237_ec50( >>> dataset = load_chembl237_ec50(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES EC50 - 1 C=CCN1CC[C@]23c4c5ccc(O)c4O[C@H]2C(=O)CC[C@@]3(O)[C@H]1C5 -0.919078 - 2 CN1CC[C@]23c4c5ccc(O)c4O[C@H]2[C@@]24CC[C@@]3(C[C@H]2C(C)(C)C(C)(C)O4)C1C5 -1.320146 - 3 CO[C@@]12CCC3(C[C@H]1[C@@](C)(O)C(C)(C)C)[C@H]1Cc4ccc(O)c5c4C3(CCN1C)[C@H]2O5 -0.380211 - 4 Nc1nc2cc3c(cc2s1)C[C@@H]1[C@@H]2CCCC[C@]32CCN1CC1CC1 -0.380211 - 5 CN1CCC23c4c5ccc(O)c4OC2c2nc(N)ncc2CC3(O)C1C5 -3.031408 + SMILES EC50 + 0 CC[C@H](C)[C@H](NC(=O)[C@H](CCCN=C(N)N)NC(=O)[... 0.130768 + 1 C=CCN1CC[C@]23c4c5ccc(O)c4O[C@H]2C(=O)CC[C@@]3... -0.919078 + 2 CN1CC[C@]23c4c5ccc(O)c4O[C@H]2[C@@]24CC[C@@]3(... -1.320146 + 3 CO[C@@]12CCC3(C[C@H]1[C@@](C)(O)C(C)(C)C)[C@H]... -0.380211 + 4 Nc1nc2cc3c(cc2s1)C[C@@H]1[C@@H]2CCCC[C@]32CCN1... -0.380211 """ df = fetch_dataset( data_dir, @@ -971,7 +961,6 @@ def load_chembl237_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Kappa-type opioid receptor target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -1015,7 +1004,7 @@ def load_chembl237_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl237_ki + >>> from skfp.datasets.moleculeace import load_chembl237_ki >>> dataset = load_chembl237_ki() >>> dataset # doctest: +SKIP (['CC(c1ccccc1)N1CC[C@H]1[C@@H](N)c1cccc(Cl)c1, ..., 'CCO[C@@]12Cc3cc(-c4ccccc4)cnc3[C@@H]3Oc4c(O)ccc5c4[C@@]31CCN(CC1CC1)[C@@H]2C5'], \ @@ -1023,12 +1012,12 @@ def load_chembl237_ki( >>> dataset = load_chembl237_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 CC(c1ccccc1)N1CC[C@H]1[C@@H](N)c1cccc(Cl)c1 -3.612678 - 1 Cc1ccc(C(c2ccc(C)cc2)N2CC[C@H]2[C@H](N)c2cccc(Cl)c2)cc1 -3.265054 - 2 COc1ccc([C@H](N)[C@@H]2CCN2C(c2ccccc2)c2ccccc2)cc1 -3.127429 - 3 N[C@H](c1cccc(Cl)c1)[C@@H]1CCN1C(c1ccc(F)cc1)c1ccc(F)cc1 -3.350248 - 4 N[C@H](c1cccc(Cl)c1)[C@@H]1CCN1C(c1cccc(Cl)c1)c1cccc(Cl)c1 -3.780821 + SMILES Ki + 0 CC(c1ccccc1)N1CC[C@H]1[C@@H](N)c1cccc(Cl)c1 -3.612678 + 1 Cc1ccc(C(c2ccc(C)cc2)N2CC[C@H]2[C@H](N)c2cccc(... -3.265054 + 2 COc1ccc([C@H](N)[C@@H]2CCN2C(c2ccccc2)c2ccccc2... -3.127429 + 3 N[C@H](c1cccc(Cl)c1)[C@@H]1CCN1C(c1ccc(F)cc1)c... -3.350248 + 4 N[C@H](c1cccc(Cl)c1)[C@@H]1CCN1C(c1cccc(Cl)c1)... -3.780821 """ df = fetch_dataset( data_dir, @@ -1057,7 +1046,6 @@ def load_chembl238_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Sodium-dependent dopamine transporter target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -1101,7 +1089,7 @@ def load_chembl238_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl238_ki + >>> from skfp.datasets.moleculeace import load_chembl238_ki >>> dataset = load_chembl238_ki() >>> dataset # doctest: +SKIP (['CN1CCC(O)(c2ccc(Cl)c(Cl)c2)C([C@@H](O)c2ccc(Cl)c(Cl)c2)C1, ..., 'C[C@H]1CN(CC[S+](O)C(c2ccc(F)cc2)c2ccc(F)cc2)C[C@@H](C)N1CC(O)Cc1ccccc1'], \ @@ -1109,12 +1097,12 @@ def load_chembl238_ki( >>> dataset = load_chembl238_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 CN1CCC(O)(c2ccc(Cl)c(Cl)c2)C([C@@H](O)c2ccc(Cl)c(Cl)c2)C1 -3.617000 - 1 CN1CCC(O)(c2ccc(Cl)c(Cl)c2)C(C(=O)c2ccc(Cl)c(Cl)c2)C1 -1.037426 - 2 Cc1ccc(C2OC(=O)OC3(c4ccc(C)cc4)CCN(C)CC23)cc1 -3.913284 - 3 Cc1ccc([C@H](O)C2CN(C)CCC2(O)c2ccc(C)cc2)cc1 -4.027350 - 4 CN1CCC(O)(c2ccc(F)cc2)C(C(=O)c2ccc(F)cc2)C1 -3.755875 + SMILES Ki + 0 CN1CCC(O)(c2ccc(Cl)c(Cl)c2)C([C@@H](O)c2ccc(Cl... -3.617000 + 1 CN1CCC(O)(c2ccc(Cl)c(Cl)c2)C(C(=O)c2ccc(Cl)c(C... -1.037426 + 2 Cc1ccc(C2OC(=O)OC3(c4ccc(C)cc4)CCN(C)CC23)cc1 -3.913284 + 3 Cc1ccc([C@H](O)C2CN(C)CCC2(O)c2ccc(C)cc2)cc1 -4.027350 + 4 CN1CCC(O)(c2ccc(F)cc2)C(C(=O)c2ccc(F)cc2)C1 -3.755875 """ df = fetch_dataset( data_dir, @@ -1143,7 +1131,6 @@ def load_chembl239_ec50( The task is to predict the half maximal effective concentration (EC50) of molecules against the Peroxisome proliferator-activated receptor alpha target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -1187,7 +1174,7 @@ def load_chembl239_ec50( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl239_ec50 + >>> from skfp.datasets.moleculeace import load_chembl239_ec50 >>> dataset = load_chembl239_ec50() >>> dataset # doctest: +SKIP (['CCC(Cc1ccc(OC)c(C(=O)NCc2ccc(OCCc3ccccc3)cc2)c1)C(=O)O, ..., 'CC(C)(Oc1ccc(CCOc2ccc(/N=N/c3ccc(Cl)cc3)cc2)cc1)C(=O)O'], \ @@ -1195,12 +1182,12 @@ def load_chembl239_ec50( >>> dataset = load_chembl239_ec50(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES EC50 - 0 CCC(Cc1ccc(OC)c(C(=O)NCc2ccc(OCCc3ccccc3)cc2)c1)C(=O)O -3.431364 - 1 CC[C@@H](Cc1ccc(OC)c(C(=O)NCc2ccc(Oc3ccc(F)cc3)cc2)c1)C(=O)O -0.964024 - 2 CCCCC(Cc1ccc(OC)c(C(=O)NCc2ccc(C(F)(F)F)cc2)c1)C(=O)O -3.000000 - 3 CCC(Cc1ccc(OC)c(C(=O)NCCc2ccc(C(F)(F)F)cc2)c1)C(=O)O -2.869232 - 4 CCC(Cc1ccc(OC)c(C(=O)NCc2ccc(OC(F)(F)F)cc2)c1)C(=O)O -1.633468 + SMILES EC50 + 0 CCC(Cc1ccc(OC)c(C(=O)NCc2ccc(OCCc3ccccc3)cc2)c... -3.431364 + 1 CC[C@@H](Cc1ccc(OC)c(C(=O)NCc2ccc(Oc3ccc(F)cc3... -0.964024 + 2 CCCCC(Cc1ccc(OC)c(C(=O)NCc2ccc(C(F)(F)F)cc2)c1... -3.000000 + 3 CCC(Cc1ccc(OC)c(C(=O)NCCc2ccc(C(F)(F)F)cc2)c1)... -2.869232 + 4 CCC(Cc1ccc(OC)c(C(=O)NCc2ccc(OC(F)(F)F)cc2)c1)... -1.633468 """ df = fetch_dataset( data_dir, @@ -1229,7 +1216,6 @@ def load_chembl244_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Coagulation factor x target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -1273,7 +1259,7 @@ def load_chembl244_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl244_ki + >>> from skfp.datasets.moleculeace import load_chembl244_ki >>> dataset = load_chembl244_ki() >>> dataset # doctest: +SKIP (['CC(=N)N1CCC(Oc2ccc3nc(CCC(=O)O)n(Cc4ccc5ccc(C(=N)N)cc5c4)c3c2)CC1, ..., 'CC(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CO)C(=O)N[C@H](C=O)CCCNC(=N)N'], \ @@ -1281,12 +1267,12 @@ def load_chembl244_ki( >>> dataset = load_chembl244_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 CC(=N)N1CCC(Oc2ccc3nc(CCC(=O)O)n(Cc4ccc5ccc(C(=N)N)cc5c4)c3c2)CC1 -0.113943 - 1 CC(=N)N1CCC(Oc2ccc3c(c2)nc(C(C)C)n3Cc2ccc3ccc(C(=N)N)cc3c2)CC1 -0.301030 - 2 CCC(C)c1nc2cc(OC3CCN(C(C)=N)CC3)ccc2n1Cc1ccc2ccc(C(=N)N)cc2c1 -0.518514 - 3 CC1CCN(C(=O)[C@H](Cc2cccc(C(=N)N)c2)NS(=O)(=O)c2c(C(C)C)cc(C(C)C)cc2C(C)C)CC1 -3.301000 - 4 COC(=O)[C@H]1Cc2ccccc2CN1C(=O)[C@H](Cc1cccc(C(=N)N)c1)NS(=O)(=O)c1ccc2ccccc2c1 -4.431000 + SMILES Ki + 0 CC(=N)N1CCC(Oc2ccc3nc(CCC(=O)O)n(Cc4ccc5ccc(C(... -0.113943 + 1 CC(=N)N1CCC(Oc2ccc3c(c2)nc(C(C)C)n3Cc2ccc3ccc(... -0.301030 + 2 CCC(C)c1nc2cc(OC3CCN(C(C)=N)CC3)ccc2n1Cc1ccc2c... -0.518514 + 3 CC1CCN(C(=O)[C@H](Cc2cccc(C(=N)N)c2)NS(=O)(=O)... -3.301000 + 4 COC(=O)[C@H]1Cc2ccccc2CN1C(=O)[C@H](Cc1cccc(C(... -4.431000 """ df = fetch_dataset( data_dir, @@ -1315,7 +1301,6 @@ def load_chembl262_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Glycogen synthase kinase-3 beta target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -1359,7 +1344,7 @@ def load_chembl262_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl262_ki + >>> from skfp.datasets.moleculeace import load_chembl262_ki >>> dataset = load_chembl262_ki() >>> dataset # doctest: +SKIP (['Cc1nc(N)sc1-c1ccnc(Nc2cccc([N+](=O)[O-])c2)n1, ..., 'CC(C)(C#N)c1cccc(-c2ccnc3[nH]ccc23)n1'], \ @@ -1367,12 +1352,12 @@ def load_chembl262_ki( >>> dataset = load_chembl262_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 Cc1nc(N)sc1-c1ccnc(Nc2cccc([N+](=O)[O-])c2)n1 -1.30103 - 1 Cc1ccc2c(-c3ccnc(Nc4cccc(C(F)(F)F)c4)n3)c(-c3ccc(F)cc3)nn2n1 -1.30103 - 2 Cc1ccc2c(-c3ccnc(Nc4ccc(F)c(F)c4)n3)c(-c3ccc(F)cc3)nn2n1 -1.00000 - 3 Cc1ccc2c(-c3ccnc(Nc4ccc5c(c4)OCCO5)n3)c(-c3ccc(F)cc3)nn2n1 -1.00000 - 4 Cc1ccc2c(-c3ccnc(Nc4ccc(Cl)c(C(F)(F)F)c4)n3)c(-c3ccc(F)cc3)nn2n1 -1.69897 + SMILES Ki + 0 Cc1nc(N)sc1-c1ccnc(Nc2cccc([N+](=O)[O-])c2)n1 -1.30103 + 1 Cc1ccc2c(-c3ccnc(Nc4cccc(C(F)(F)F)c4)n3)c(-c3c... -1.30103 + 2 Cc1ccc2c(-c3ccnc(Nc4ccc(F)c(F)c4)n3)c(-c3ccc(F... -1.00000 + 3 Cc1ccc2c(-c3ccnc(Nc4ccc5c(c4)OCCO5)n3)c(-c3ccc... -1.00000 + 4 Cc1ccc2c(-c3ccnc(Nc4ccc(Cl)c(C(F)(F)F)c4)n3)c(... -1.69897 """ df = fetch_dataset( data_dir, @@ -1401,7 +1386,6 @@ def load_chembl264_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Histamine h3 receptor target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -1445,7 +1429,7 @@ def load_chembl264_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl264_ki + >>> from skfp.datasets.moleculeace import load_chembl264_ki >>> dataset = load_chembl264_ki() >>> dataset # doctest: +SKIP (['CC(=O)c1ccc(OCCCc2c[nH]cn2)cc1, ..., 'CC(C)(C)c1ccc(OCCCCCCN2CCCCCC2)cc1'], \ @@ -1487,7 +1471,6 @@ def load_chembl287_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Sigma non-opioid intracellular receptor 1 target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -1531,7 +1514,7 @@ def load_chembl287_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl287_ki + >>> from skfp.datasets.moleculeace import load_chembl287_ki >>> dataset = load_chembl287_ki() >>> dataset # doctest: +SKIP (['O=S1(=O)c2ccccc2CCC12CCN(Cc1ccccc1)CC2, ..., 'Cc1[nH]c2cc(C(F)(F)F)ccc2c(=O)c1CN(C)Cc1ccccc1'], \ @@ -1539,12 +1522,12 @@ def load_chembl287_ki( >>> dataset = load_chembl287_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 O=S1(=O)c2ccccc2CCC12CCN(Cc1ccccc1)CC2 -1.301030 - 1 COc1ccc(N2C[C@H](CN3CCC(O)(c4ccsc4)CC3)OC2=O)cc1 -1.531479 - 2 COc1ccc(N2C[C@H](CN3CCC(O)(c4ccc5c(c4)OCO5)CC3)OC2=O)cc1 -1.278754 - 3 CNC(=O)CC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1 -2.230449 - 4 OCC1OC2(CCN(Cc3ccccc3)CC2)c2ccccc21 -0.752816 + SMILES Ki + 0 O=S1(=O)c2ccccc2CCC12CCN(Cc1ccccc1)CC2 -1.301030 + 1 COc1ccc(N2C[C@H](CN3CCC(O)(c4ccsc4)CC3)OC2=O)cc1 -1.531479 + 2 COc1ccc(N2C[C@H](CN3CCC(O)(c4ccc5c(c4)OCO5)CC3... -1.278754 + 3 CNC(=O)CC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1 -2.230449 + 4 OCC1OC2(CCN(Cc3ccccc3)CC2)c2ccccc21 -0.752816 """ df = fetch_dataset( data_dir, @@ -1573,7 +1556,6 @@ def load_chembl1862_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Tyrosine-protein kinase abl1 target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -1617,7 +1599,7 @@ def load_chembl1862_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl1862_ki + >>> from skfp.datasets.moleculeace import load_chembl1862_ki >>> dataset = load_chembl1862_ki() >>> dataset # doctest: +SKIP (['Nc1[nH]cnc2nnc(-c3ccc(Cl)cc3)c1-2, ..., 'CCCCNc1ncnc2c1cnn2CC(Cl)c1ccccc1'], \ @@ -1625,12 +1607,12 @@ def load_chembl1862_ki( >>> dataset = load_chembl1862_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 Nc1[nH]cnc2nnc(-c3ccc(Cl)cc3)c1-2 -2.69897 - 1 Cc1ccc(N2NC(=O)/C(=C/c3ccc(-c4ccc(C)c(Cl)c4)o3)C2=O)cc1C -3.69897 - 2 O=C1NN(c2ccc(Cl)c(Cl)c2)C(=O)/C1=C\c1cccc(OCc2ccccc2)c1 -3.00000 - 3 O=C1NN(c2ccc(I)cc2)C(=O)/C1=C\c1cc2c(cc1Br)OCO2 -3.39794 - 4 O=C1NN(c2ccc(I)cc2)C(=O)/C1=C\c1ccc(N2CCOCC2)cc1 -4.30103 + SMILES Ki + 0 Nc1[nH]cnc2nnc(-c3ccc(Cl)cc3)c1-2 -2.69897 + 1 Cc1ccc(N2NC(=O)/C(=C/c3ccc(-c4ccc(C)c(Cl)c4)o3... -3.69897 + 2 O=C1NN(c2ccc(Cl)c(Cl)c2)C(=O)/C1=C\c1cccc(OCc2... -3.00000 + 3 O=C1NN(c2ccc(I)cc2)C(=O)/C1=C\c1cc2c(cc1Br)OCO2 -3.39794 + 4 O=C1NN(c2ccc(I)cc2)C(=O)/C1=C\c1ccc(N2CCOCC2)cc1 -4.30103 """ df = fetch_dataset( data_dir, @@ -1659,7 +1641,6 @@ def load_chembl1871_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Androgen receptor target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -1703,7 +1684,7 @@ def load_chembl1871_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl1871_ki + >>> from skfp.datasets.moleculeace import load_chembl1871_ki >>> dataset = load_chembl1871_ki() >>> dataset # doctest: +SKIP (['CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccsc1)Oc1ccc(F)cc1-3, ..., 'CN(C[C@](C)(O)C(=O)Nc1ccc(C#N)c(C(F)(F)F)c1)c1ccc(C#N)c(-c2ccccc2)c1'], \ @@ -1711,12 +1692,12 @@ def load_chembl1871_ki( >>> dataset = load_chembl1871_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccsc1)Oc1ccc(F)cc1-3 -2.825426 - 1 CCc1ccccc1/C=C1\Oc2ccc(F)cc2-c2ccc3c(c21)C(C)=CC(C)(C)N3 -3.201124 - 2 CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccccc1N(C)C)Oc1ccc(F)cc1-3 -2.913284 - 3 CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccccc1)Oc1c(F)cccc1-3 -3.163161 - 4 CC(=O)O[C@]1(C(C)=O)CC[C@H]2[C@@H]3C[C@H](C)C4=CC(=O)CC[C@]4(C)[C@H]3CC[C@@]21C -0.462398 + SMILES Ki + 0 CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccsc1)Oc1ccc(... -2.825426 + 1 CCc1ccccc1/C=C1\Oc2ccc(F)cc2-c2ccc3c(c21)C(C)=... -3.201124 + 2 CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccccc1N(C)C)O... -2.913284 + 3 CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccccc1)Oc1c(F... -3.163161 + 4 CC(=O)O[C@]1(C(C)=O)CC[C@H]2[C@@H]3C[C@H](C)C4... -0.462398 """ df = fetch_dataset( data_dir, @@ -1745,7 +1726,6 @@ def load_chembl2034_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Glucocorticoid receptor target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -1789,7 +1769,7 @@ def load_chembl2034_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl2034_ki + >>> from skfp.datasets.moleculeace import load_chembl2034_ki >>> dataset = load_chembl2034_ki() >>> dataset # doctest: +SKIP (['CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccsc1)Oc1ccc(F)cc1-3, ..., 'NS(=O)(=O)C[C@H]1COc2cc(F)ccc2N1C(=O)c1ccc2c(c1)NCCO2'], \ @@ -1797,12 +1777,12 @@ def load_chembl2034_ki( >>> dataset = load_chembl2034_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccsc1)Oc1ccc(F)cc1-3 -1.924279 - 1 CCc1ccccc1/C=C1\Oc2ccc(F)cc2-c2ccc3c(c21)C(C)=CC(C)(C)N3 -2.431364 - 2 CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccccc1N(C)C)Oc1ccc(F)cc1-3 -2.692847 - 3 CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccccc1)Oc1c(F)cccc1-3 -2.506505 - 4 CC(=O)O[C@]1(C(C)=O)CC[C@H]2[C@@H]3C[C@H](C)C4=CC(=O)CC[C@]4(C)[C@H]3CC[C@@]21C -1.120574 + SMILES Ki + 0 CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccsc1)Oc1ccc(... -1.924279 + 1 CCc1ccccc1/C=C1\Oc2ccc(F)cc2-c2ccc3c(c21)C(C)=... -2.431364 + 2 CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccccc1N(C)C)O... -2.692847 + 3 CC1=CC(C)(C)Nc2ccc3c(c21)/C(=C/c1ccccc1)Oc1c(F... -2.506505 + 4 CC(=O)O[C@]1(C(C)=O)CC[C@H]2[C@@H]3C[C@H](C)C4... -1.120574 """ df = fetch_dataset( data_dir, @@ -1831,7 +1811,6 @@ def load_chembl2047_ec50( The task is to predict the half maximal effective concentration (EC50) of molecules against the Bile acid receptor target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -1875,7 +1854,7 @@ def load_chembl2047_ec50( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl2047_ec50 + >>> from skfp.datasets.moleculeace import load_chembl2047_ec50 >>> dataset = load_chembl2047_ec50() >>> dataset # doctest: +SKIP (['C[C@H](CCC(=O)NCC(=O)O)[C@H]1CC[C@H]2[C@H]3[C@H](CC[C@@]21C)[C@@]1(C)CC[C@@H](O)C[C@H]1C[C@H]3O, ..., 'CC(C)c1onc(-c2c(Cl)cccc2Cl)c1COc1ccc(CNc2ccc(CC(=O)O)cc2)c(Cl)c1'], \ @@ -1883,12 +1862,12 @@ def load_chembl2047_ec50( >>> dataset = load_chembl2047_ec50(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES EC50 - 0 C[C@H](CCC(=O)NCC(=O)O)[C@H]1CC[C@H]2[C@H]3[C@H](CC[C@@]21C)[C@@]1(C)CC[C@@H](O)C[C@H]1C[C@H]3O -3.477121 - 1 C[C@H](CCC(=O)O)C1CC[C@H]2[C@H]3[C@H](CC[C@]12C)[C@@]1(C)CC[C@@H](O)CC1[C@@H](C)[C@H]3O -2.875061 - 3 CCC[C@@H]1C2C[C@H](O)CC[C@]2(C)[C@H]2CC[C@]3(C)C([C@H](C)CCC(=O)O)CC[C@H]3[C@@H]2[C@@H]1O -3.045323 - 4 CC(C)c1onc(-c2c(Cl)cccc2Br)c1COc1ccc(/C=C/c2cccc(C(=O)O)c2)c(Cl)c1 -1.079181 - 5 Cc1cc(OCc2c(-c3c(Cl)cccc3Cl)noc2C(C)C)ccc1/C=C/c1cccc(C(=O)O)c1 -1.672098 + SMILES EC50 + 0 C[C@H](CCC(=O)NCC(=O)O)[C@H]1CC[C@H]2[C@H]3[C@... -3.477121 + 1 C[C@H](CCC(=O)O)C1CC[C@H]2[C@H]3[C@H](CC[C@]12... -2.875061 + 2 C[C@H](CCC(=O)NCCS(=O)(=O)O)[C@H]1CC[C@H]2[C@H... -3.477121 + 3 CCC[C@@H]1C2C[C@H](O)CC[C@]2(C)[C@H]2CC[C@]3(C... -3.045323 + 4 CC(C)c1onc(-c2c(Cl)cccc2Br)c1COc1ccc(/C=C/c2cc... -1.079181 """ df = fetch_dataset( data_dir, @@ -1917,7 +1896,6 @@ def load_chembl2147_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Serine/threonine-protein kinase pim-1 target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -1961,7 +1939,7 @@ def load_chembl2147_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl2147_ki + >>> from skfp.datasets.moleculeace import load_chembl2147_ki >>> dataset = load_chembl2147_ki() >>> dataset # doctest: +SKIP (['FC(F)(F)c1cccc(-c2nnc3ccc(NC4CCCCC4)cn23)c1, ..., 'NC(=O)c1cc(Cl)c2c(Cl)c(C#CC3CNCCO3)n([C@H]3CCCNC3)c2n1'], \ @@ -1969,12 +1947,12 @@ def load_chembl2147_ki( >>> dataset = load_chembl2147_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 FC(F)(F)c1cccc(-c2nnc3ccc(NC4CCCCC4)cn23)c1 -1.041393 - 1 Cc1ccc2[nH]c(=O)c(CC(=O)O)c(-c3ccccc3)c2c1 -3.653213 - 2 O=C(O)c1cccc(Nc2nc(-c3ccc(O)cc3O)cs2)c1 -3.531479 - 3 O=C(O)c1cccc2c(-c3ccccc3)c(-c3ccccc3)[nH]c12 -2.740363 - 4 CCc1ccc(C2C(C(C)=O)=C(O)C(=O)N2CCc2c[nH]c3ccccc23)cc1 -3.322219 + SMILES Ki + 0 FC(F)(F)c1cccc(-c2nnc3ccc(NC4CCCCC4)cn23)c1 -1.041393 + 1 Cc1ccc2[nH]c(=O)c(CC(=O)O)c(-c3ccccc3)c2c1 -3.653213 + 2 O=C(O)c1cccc(Nc2nc(-c3ccc(O)cc3O)cs2)c1 -3.531479 + 3 O=C(O)c1cccc2c(-c3ccccc3)c(-c3ccccc3)[nH]c12 -2.740363 + 4 CCc1ccc(C2C(C(C)=O)=C(O)C(=O)N2CCc2c[nH]c3cccc... -3.322219 """ df = fetch_dataset( data_dir, @@ -2003,7 +1981,6 @@ def load_chembl2835_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Tyrosine-protein kinase jak1 target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -2047,7 +2024,7 @@ def load_chembl2835_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl2835_ki + >>> from skfp.datasets.moleculeace import load_chembl2835_ki >>> dataset = load_chembl2835_ki() >>> dataset # doctest: +SKIP (['C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12, ..., 'Cc1cnc(Nc2ccc(OCCN3CCCC3)cc2)nc1Nc1cccc(S(=O)(=O)NC(C)(C)C)c1'], \ @@ -2055,12 +2032,12 @@ def load_chembl2835_ki( >>> dataset = load_chembl2835_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12 0.154902 - 1 C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1n1cnc2cnc3[nH]ccc3c21 0.301030 - 2 C[C@@H]1CCN(Cc2ccccc2)C[C@@H]1N(C)c1ncnc2[nH]ccc12 -2.785330 - 3 C[C@@H]1CCN(Cc2ccccc2)C[C@@H]1n1cnc2cnc3[nH]ccc3c21 -1.079181 - 4 N#CCC(=O)N1CCC[C@@H](n2cnc3cnc4[nH]ccc4c32)C1 0.397940 + SMILES Ki + 0 C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]c... 0.154902 + 1 C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1n1cnc2cnc3[nH]cc... 0.301030 + 2 C[C@@H]1CCN(Cc2ccccc2)C[C@@H]1N(C)c1ncnc2[nH]c... -2.785330 + 3 C[C@@H]1CCN(Cc2ccccc2)C[C@@H]1n1cnc2cnc3[nH]cc... -1.079181 + 4 N#CCC(=O)N1CCC[C@@H](n2cnc3cnc4[nH]ccc4c32)C1 0.397940 """ df = fetch_dataset( data_dir, @@ -2089,7 +2066,6 @@ def load_chembl2971_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Tyrosine-protein kinase jak2 target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -2133,7 +2109,7 @@ def load_chembl2971_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl2971_ki + >>> from skfp.datasets.moleculeace import load_chembl2971_ki >>> dataset = load_chembl2971_ki() >>> dataset # doctest: +SKIP (['NC(=O)Nc1sc(-c2ccc(F)cc2)cc1C(N)=O, ..., 'Cc1cc(Nc2nc(N[C@@H](C)c3ccc(F)cc3)c(C#N)cc2F)n[nH]1'], \ @@ -2141,12 +2117,12 @@ def load_chembl2971_ki( >>> dataset = load_chembl2971_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 NC(=O)Nc1sc(-c2ccc(F)cc2)cc1C(N)=O -0.698970 - 1 O[C@H]1CC[C@H](Nc2ccc3nnc(-c4cccc(C(F)(F)F)c4)n3n2)CC1 -3.380211 - 2 c1ccc(-c2ncnc3[nH]ccc23)cc1 -2.683947 - 3 Clc1cnc2[nH]cc(-c3ccccc3)c2c1 -2.414973 - 4 CCC1Nc2ccccc2-c2ccnc3[nH]cc1c23 -3.230449 + SMILES Ki + 0 NC(=O)Nc1sc(-c2ccc(F)cc2)cc1C(N)=O -0.698970 + 1 O[C@H]1CC[C@H](Nc2ccc3nnc(-c4cccc(C(F)(F)F)c4)... -3.380211 + 2 c1ccc(-c2ncnc3[nH]ccc23)cc1 -2.683947 + 3 Clc1cnc2[nH]cc(-c3ccccc3)c2c1 -2.414973 + 4 CCC1Nc2ccccc2-c2ccnc3[nH]cc1c23 -3.230449 """ df = fetch_dataset( data_dir, @@ -2175,7 +2151,6 @@ def load_chembl3979_ec50( The task is to predict the half maximal effective concentration (EC50) of molecules against the Peroxisome proliferator-activated receptor delta target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -2219,7 +2194,7 @@ def load_chembl3979_ec50( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl3979_ec50 + >>> from skfp.datasets.moleculeace import load_chembl3979_ec50 >>> dataset = load_chembl3979_ec50() >>> dataset # doctest: +SKIP (['CCC(Cc1ccc(OC)c(C(=O)NCCc2ccc(C(F)(F)F)cc2)c1)C(=O)O, ..., 'CC(C)c1onc(-c2c(Cl)cccc2Cl)c1COc1ccc(CNc2ccc(CC(=O)O)cc2)c(Cl)c1'], \ @@ -2227,12 +2202,12 @@ def load_chembl3979_ec50( >>> dataset = load_chembl3979_ec50(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES EC50 - 0 CCC(Cc1ccc(OC)c(C(=O)NCCc2ccc(C(F)(F)F)cc2)c1)C(=O)O -3.176091 - 1 CCC(Cc1ccc(OC)c(C(=O)NCc2ccc(OC(F)(F)F)cc2)c1)C(=O)O -2.954243 - 2 CCC(Cc1ccc(OC)c(CCCc2ccc(C(F)(F)F)cc2)c1)C(=O)O -2.806180 - 3 CCSC(Cc1ccc(OC)c(C(=O)NCc2ccc(C(F)(F)F)cc2)c1)C(=O)O -3.477121 - 4 CCOC(Cc1ccc(OC)c(C(=O)NCc2ccc(C(F)(F)F)cc2)c1)C(=O)O -3.477121 + SMILES EC50 + 0 CCC(Cc1ccc(OC)c(C(=O)NCCc2ccc(C(F)(F)F)cc2)c1)... -3.176091 + 1 CCC(Cc1ccc(OC)c(C(=O)NCc2ccc(OC(F)(F)F)cc2)c1)... -2.954243 + 2 CCC(Cc1ccc(OC)c(CCCc2ccc(C(F)(F)F)cc2)c1)C(=O)O -2.806180 + 3 CCSC(Cc1ccc(OC)c(C(=O)NCc2ccc(C(F)(F)F)cc2)c1)... -3.477121 + 4 CCOC(Cc1ccc(OC)c(C(=O)NCc2ccc(C(F)(F)F)cc2)c1)... -3.477121 """ df = fetch_dataset( data_dir, @@ -2261,7 +2236,6 @@ def load_chembl4005_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -2305,7 +2279,7 @@ def load_chembl4005_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl4005_ki + >>> from skfp.datasets.moleculeace import load_chembl4005_ki >>> dataset = load_chembl4005_ki() >>> dataset # doctest: +SKIP (['COC[C@H]1OC(=O)c2coc3c2[C@@]1(C)C1=C(C3=O)[C@@H]2CCC(=O)[C@@]2(C)C[C@H]1OC(C)=O, ..., 'CC(C)n1nc(-c2ccc3oc(N)nc3c2)c2c(N)ncnc21'], \ @@ -2313,12 +2287,12 @@ def load_chembl4005_ki( >>> dataset = load_chembl4005_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 COC[C@H]1OC(=O)c2coc3c2[C@@]1(C)C1=C(C3=O)[C@@H]2CCC(=O)[C@@]2(C)C[C@H]1OC(C)=O -2.079181 - 1 O=c1cc(N2CCOCC2)oc2c(-c3ccccc3)cccc12 -3.778151 - 2 CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1 -0.806180 - 3 COc1ccc(NC(=O)c2c(C)ccc3c(N)nc(C)nc23)cn1 -2.000000 - 4 COc1ccc(NC(=O)c2cc(C)cc3c(N)nc(C)nc23)cn1 0.301030 + SMILES Ki + 0 COC[C@H]1OC(=O)c2coc3c2[C@@]1(C)C1=C(C3=O)[C@@... -2.079181 + 1 O=c1cc(N2CCOCC2)oc2c(-c3ccccc3)cccc12 -3.778151 + 2 CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(... -0.806180 + 3 COc1ccc(NC(=O)c2c(C)ccc3c(N)nc(C)nc23)cn1 -2.000000 + 4 COc1ccc(NC(=O)c2cc(C)cc3c(N)nc(C)nc23)cn1 0.301030 """ df = fetch_dataset( data_dir, @@ -2347,7 +2321,6 @@ def load_chembl4203_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Dual specificity protein kinase clk4 target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -2391,7 +2364,7 @@ def load_chembl4203_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl4203_ki + >>> from skfp.datasets.moleculeace import load_chembl4203_ki >>> dataset = load_chembl4203_ki() >>> dataset # doctest: +SKIP (['O=c1[nH]cnc2c1sc1c(Cl)ccc(Cl)c12, ..., 'O=C(c1cccc(-c2cnc3[nH]ccc3c2)c1)N1CCOCC1'], \ @@ -2399,12 +2372,12 @@ def load_chembl4203_ki( >>> dataset = load_chembl4203_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 O=c1[nH]cnc2c1sc1c(Cl)ccc(Cl)c12 -1.976808 - 1 Nc1ncnc2onc(-c3ccc(NC(=O)Nc4cccc(C(F)(F)F)c4)cc3)c12 -2.400002 - 2 O=c1[nH]cnc2c(-c3ccccc3)c(C(F)(F)F)sc12 -3.299999 - 3 O=C1Nc2ccccc2Nc2cc(-c3ccncc3F)ccc21 -1.400020 - 4 Cc1cc(N2CCOCC2)cc2[nH]c(-c3c(NCC(O)c4cccc(Cl)c4)cc[nH]c3=O)nc12 -1.700011 + SMILES Ki + 0 O=c1[nH]cnc2c1sc1c(Cl)ccc(Cl)c12 -1.976808 + 1 Nc1ncnc2onc(-c3ccc(NC(=O)Nc4cccc(C(F)(F)F)c4)c... -2.400002 + 2 O=c1[nH]cnc2c(-c3ccccc3)c(C(F)(F)F)sc12 -3.299999 + 3 O=C1Nc2ccccc2Nc2cc(-c3ccncc3F)ccc21 -1.400020 + 4 Cc1cc(N2CCOCC2)cc2[nH]c(-c3c(NCC(O)c4cccc(Cl)c... -1.700011 """ df = fetch_dataset( data_dir, @@ -2433,7 +2406,6 @@ def load_chembl4616_ec50( The task is to predict the half maximal effective concentration (EC50) of molecules against the Growth hormone secretagogue receptor type 1 target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -2477,7 +2449,7 @@ def load_chembl4616_ec50( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl4616_ec50 + >>> from skfp.datasets.moleculeace import load_chembl4616_ec50 >>> dataset = load_chembl4616_ec50() >>> dataset # doctest: +SKIP (['CCCCCCCC(=O)OC[C@H](NC(=O)CN)C(=O)N[C@@H](CO)C(=O)N[C@@H](Cc1ccccc1)C(=O)O, ..., 'CC(=O)N1CCC[C@H](NC(=O)[C@H]2CN(S(=O)(=O)c3ccccc3)C[C@@H]2NC(=O)c2cc(-c3ccccc3Cl)on2)C1'], \ @@ -2485,12 +2457,12 @@ def load_chembl4616_ec50( >>> dataset = load_chembl4616_ec50(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES EC50 - 0 CCCCCCCC(=O)OC[C@H](NC(=O)CN)C(=O)N[C@@H](CO)C(=O)N[C@@H](Cc1ccccc1)C(=O)O -1.857332 - 2 CC(C)(N)C(=O)N[C@H](COCc1ccccc1)C(=O)N1CCC2(CC1)CN(S(C)(=O)=O)c1ccccc12 0.072578 - 3 NC(=O)CN(CCc1ccccc1)C(=O)[C@@H](Cc1ccc2ccccc2c1)NC(=O)[C@@H](Cc1ccc2ccccc2c1)NC(=O)C1CCNCC1 0.468521 - 4 CC(C)N(CCNC(=O)C1c2ccc(Oc3cccc(F)c3)cc2CCN1C(=O)OC(C)(C)C)C(C)C -0.633468 - 5 CC(C)N(CCNC(=O)C1c2ccc(Oc3ccc(Cl)cc3)cc2CCN1C(=O)OC(C)(C)C)C(C)C 0.136677 + SMILES EC50 + 0 CCCCCCCC(=O)OC[C@H](NC(=O)CN)C(=O)N[C@@H](CO)C... -1.857332 + 1 CCCCCCCC(=O)OC[C@H](NC(=O)CNC(=O)[C@@H](N)CCCN... -0.147985 + 2 CC(C)(N)C(=O)N[C@H](COCc1ccccc1)C(=O)N1CCC2(CC... 0.072578 + 3 NC(=O)CN(CCc1ccccc1)C(=O)[C@@H](Cc1ccc2ccccc2c... 0.468521 + 4 CC(C)N(CCNC(=O)C1c2ccc(Oc3cccc(F)c3)cc2CCN1C(=... -0.633468 """ df = fetch_dataset( data_dir, @@ -2519,7 +2491,6 @@ def load_chembl4792_ki( The task is to predict the inhibitor constant (Ki) of molecules against the Orexin receptor type 2 target [1]_ [2]_. - ================== ============== Tasks 1 Task type regression @@ -2563,7 +2534,7 @@ def load_chembl4792_ki( Examples -------- - >>> from skfp.datasets.moleculenet import load_chembl4792_ki + >>> from skfp.datasets.moleculeace import load_chembl4792_ki >>> dataset = load_chembl4792_ki() >>> dataset # doctest: +SKIP (['CC1(C)OC[C@H](NC(=O)Nc2ccc(Br)cc2Cl)[C@H](c2ccccc2)O1, ..., 'CC(/C=C/c1ccccc1)=N/Nc1nc(Nc2ccccc2)nc(-n2nc(C)cc2C)n1'], \ @@ -2571,12 +2542,12 @@ def load_chembl4792_ki( >>> dataset = load_chembl4792_ki(as_frame=True) >>> dataset.head() # doctest: +NORMALIZE_WHITESPACE - SMILES Ki - 0 CC1(C)OC[C@H](NC(=O)Nc2ccc(Br)cc2Cl)[C@H](c2ccccc2)O1 -0.800029 - 1 Cc1cc(Br)ccc1NC(=O)N[C@H]1COC(C)(C)O[C@H]1c1ccccc1 -1.599992 - 2 Cc1ccc(Cl)c(NC(=O)N[C@H]2COC(C)(C)O[C@H]2c2ccccc2)c1 -1.800029 - 3 Cc1ccc(NC(=O)N[C@H]2COC(C)(C)O[C@H]2c2ccccc2)c(C)c1 -2.099991 - 4 CC1(C)OC[C@H](NC(=O)Nc2cc(Cl)ccc2Cl)[C@H](c2ccccc2)O1 -1.800029 + SMILES Ki + 0 CC1(C)OC[C@H](NC(=O)Nc2ccc(Br)cc2Cl)[C@H](c2cc... -0.800029 + 1 Cc1cc(Br)ccc1NC(=O)N[C@H]1COC(C)(C)O[C@H]1c1cc... -1.599992 + 2 Cc1ccc(Cl)c(NC(=O)N[C@H]2COC(C)(C)O[C@H]2c2ccc... -1.800029 + 3 Cc1ccc(NC(=O)N[C@H]2COC(C)(C)O[C@H]2c2ccccc2)c... -2.099991 + 4 CC1(C)OC[C@H](NC(=O)Nc2cc(Cl)ccc2Cl)[C@H](c2cc... -1.800029 """ df = fetch_dataset( data_dir, diff --git a/skfp/datasets/moleculenet/pcba.py b/skfp/datasets/moleculenet/pcba.py index 9f0a2e81..dcf927d2 100644 --- a/skfp/datasets/moleculenet/pcba.py +++ b/skfp/datasets/moleculenet/pcba.py @@ -41,7 +41,7 @@ def load_pcba( not read correctly due to disallowed hypervalent states of some atoms (see [release notes](https://github.com/rdkit/rdkit/releases/tag/Release_2024_09_1)). This version of the PCBA dataset contains manual fixes for those molecules, removing - additional hydrogens, e.g. `[AlH3] -> [Al]`. In OGB scaffold split, used for + additional hydrogens, e.g. ``[AlH3] -> [Al]``. In OGB scaffold split, used for benchmarking, both molecules are in the training set. Parameters diff --git a/skfp/datasets/moleculenet/tox21.py b/skfp/datasets/moleculenet/tox21.py index dedaef8f..8f76d158 100644 --- a/skfp/datasets/moleculenet/tox21.py +++ b/skfp/datasets/moleculenet/tox21.py @@ -41,7 +41,7 @@ def load_tox21( are not read correctly due to disallowed hypervalent states of their aluminium atoms (see [release notes](https://github.com/rdkit/rdkit/releases/tag/Release_2024_09_1)). This version of the Tox21 dataset contains manual fixes for those molecules, - removing additional hydrogens, e.g. `[AlH3] -> [Al]`. In OGB scaffold split, used + removing additional hydrogens, e.g. ``[AlH3] -> [Al]``. In OGB scaffold split, used for benchmarking, only the first 1 of those problematic 8 is from the test set. Parameters diff --git a/skfp/descriptors/charge.py b/skfp/descriptors/charge.py index 4106c8a5..065eec3c 100644 --- a/skfp/descriptors/charge.py +++ b/skfp/descriptors/charge.py @@ -18,11 +18,13 @@ def atomic_partial_charges( Parameters ---------- mol : RDKit ``Mol`` object - The molecule for which the Balaban's J index is to be calculated. + The molecule for which to calculate the atomic partial charges. partial_charge_model : {"Gasteiger", "MMFF94", "formal", "precomputed"}, default="formal" Which model to use to compute atomic partial charges. Default ``"formal"`` computes formal charges, and is the simplest and most error-resistant one. + ``"precomputed"`` assumes that the inputs are RDKit ``PropertyMol`` objects + with "charge" float property set. charge_errors : {"raise", "ignore", "zero"}, default="raise" How to handle errors during calculation of atomic partial charges. ``"raise"`` @@ -51,6 +53,8 @@ def atomic_partial_charges( ] elif partial_charge_model == "formal": charges = [atom.GetFormalCharge() for atom in atoms] + elif partial_charge_model == "precomputed": + charges = [atom.GetDoubleProp("charge") for atom in atoms] else: raise ValueError( f'Partial charge model "{partial_charge_model}" is not supported' diff --git a/skfp/descriptors/constitutional.py b/skfp/descriptors/constitutional.py index 554a04aa..75bd4716 100644 --- a/skfp/descriptors/constitutional.py +++ b/skfp/descriptors/constitutional.py @@ -47,7 +47,7 @@ def bond_count(mol: Mol, bond_type: str | None = None) -> int: mol : RDKit ``Mol`` object The molecule for which the bond count is to be calculated. - bond_type : str, optional + bond_type : str, default=None If ``None``, returns the total number of bonds. Otherwise, the valid options are RDKit bond types [1]_, e.g. "SINGLE", "DOUBLE", "TRIPLE", "AROMATIC". diff --git a/skfp/descriptors/topological.py b/skfp/descriptors/topological.py index d129d6f6..6dc274b8 100644 --- a/skfp/descriptors/topological.py +++ b/skfp/descriptors/topological.py @@ -21,7 +21,7 @@ def average_wiener_index(mol: Mol, distance_matrix: np.ndarray | None = None) -> mol : RDKit ``Mol`` object The molecule for which the Average Wiener index is to be calculated. - distance_matrix : np.ndarray, optional + distance_matrix : np.ndarray, default=None Precomputed distance matrix. If not provided, it will be calculated using RDKit. References @@ -70,7 +70,7 @@ def balaban_j_index(mol: Mol, distance_matrix: np.ndarray | None = None) -> floa mol : RDKit ``Mol`` object The molecule for which the Balaban's J index is to be calculated. - distance_matrix : np.ndarray, optional + distance_matrix : np.ndarray, default=None Precomputed distance matrix. If not provided, it will be calculated by RDKit. References @@ -118,7 +118,7 @@ def burden_matrix(mol: Mol, descriptors: np.ndarray | None = None) -> np.ndarray mol : RDKit ``Mol`` object The molecule for which the Balaban's J index is to be calculated. - descriptors : np.ndarray, optional + descriptors : np.ndarray, default=None Vector of atomic descriptors, with the same length as number of atoms in the input molecule. @@ -189,7 +189,7 @@ def diameter(mol: Mol, distance_matrix: np.ndarray | None = None) -> int: mol : RDKit ``Mol`` object The molecule for which the diameter is to be calculated. - distance_matrix : np.ndarray, optional + distance_matrix : np.ndarray, default=None Precomputed distance matrix. If not provided, it will be calculated. References @@ -238,7 +238,7 @@ def graph_distance_index(mol: Mol, distance_matrix: np.ndarray | None = None) -> mol : RDKit ``Mol`` object The molecule for which the Graph Distance Index is to be calculated. - distance_matrix : np.ndarray, optional + distance_matrix : np.ndarray, default=None Precomputed distance matrix. If not provided, it will be calculated using RDKit. References @@ -323,7 +323,7 @@ def petitjean_index(mol: Mol, distance_matrix: np.ndarray | None = None) -> floa mol : RDKit ``Mol`` object The molecule for which the Petitjean index is to be calculated. - distance_matrix : np.ndarray, optional + distance_matrix : np.ndarray, default=None Precomputed distance matrix. If not provided, it will be calculated. References @@ -364,7 +364,7 @@ def polarity_number( mol : RDKit ``Mol`` object The molecule for which the Polarity Number is to be calculated. - distance_matrix : np.ndarray, optional + distance_matrix : np.ndarray, default=None Precomputed distance matrix. If not provided, it will be calculated using RDKit. carbon_only : bool, default=False @@ -544,7 +544,7 @@ def radius(mol: Mol, distance_matrix: np.ndarray | None = None) -> int: mol : RDKit ``Mol`` object The molecule for which the radius is to be calculated. - distance_matrix : np.ndarray, optional + distance_matrix : np.ndarray, default=None Precomputed distance matrix. If not provided, it will be calculated. References @@ -583,7 +583,7 @@ def wiener_index(mol: Mol, distance_matrix: np.ndarray | None = None) -> int: mol : RDKit ``Mol`` object The molecule for which the Wiener index is to be calculated. - distance_matrix : np.ndarray, optional + distance_matrix : np.ndarray, default=None Precomputed distance matrix. If not provided, it will be calculated using RDKit. References diff --git a/skfp/distances/braun_blanquet.py b/skfp/distances/braun_blanquet.py index fccd759d..e59f02a9 100644 --- a/skfp/distances/braun_blanquet.py +++ b/skfp/distances/braun_blanquet.py @@ -5,8 +5,8 @@ @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -91,8 +91,8 @@ def braun_blanquet_binary_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -163,7 +163,7 @@ def braun_blanquet_binary_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -177,17 +177,17 @@ def bulk_braun_blanquet_binary_similarity( Computes the pairwise Braun-Blanquet similarity between binary matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`braun_blanquet_binary_similarity`. Parameters ---------- X : ndarray or CSR sparse array - First binary input array or sparse matrix, of shape :math:`m \times m` + First binary input array or sparse matrix, of shape :math:`m \times d` Y : ndarray or CSR sparse array, default=None - Second binary input array or sparse matrix, of shape :math:`n \times n`. If not passed, + Second binary input array or sparse matrix, of shape :math:`n \times d`. If not passed, similarities are computed between rows of X. Returns @@ -250,7 +250,7 @@ def _bulk_braun_blanquet_binary_similarity_two( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -264,17 +264,17 @@ def bulk_braun_blanquet_binary_distance( Computes the pairwise Braun-Blanquet distance between binary matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`braun_blanquet_binary_distance`. Parameters ---------- X : ndarray or CSR sparse array - First binary input array, of shape :math:`m \times m` + First binary input array, of shape :math:`m \times d` Y : ndarray or CSR sparse array, default=None - Second binary input array, of shape :math:`n \times n`. If not passed, distances + Second binary input array, of shape :math:`n \times d`. If not passed, distances are computed between rows of X. Returns diff --git a/skfp/distances/ct4.py b/skfp/distances/ct4.py index dd2d1642..c508e759 100644 --- a/skfp/distances/ct4.py +++ b/skfp/distances/ct4.py @@ -5,8 +5,8 @@ @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -97,8 +97,8 @@ def ct4_binary_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -174,8 +174,8 @@ def ct4_binary_distance( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -268,8 +268,8 @@ def ct4_count_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -345,7 +345,7 @@ def ct4_count_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -359,7 +359,7 @@ def bulk_ct4_binary_similarity( Computes the pairwise Consonni–Todeschini 4 (CT4) similarity between binary matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`ct4_binary_similarity`. @@ -434,7 +434,7 @@ def _bulk_ct4_binary_similarity_two(X: csr_array, Y: csr_array) -> np.ndarray: @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -448,7 +448,7 @@ def bulk_ct4_binary_distance( Computes the pairwise Consonni–Todeschini 4 (CT4) distance between binary matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column - in output corresponding to `i`-th row from first array and `j`-th row from second array. + in output corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`ct4_binary_distance`. @@ -472,7 +472,7 @@ def bulk_ct4_binary_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -486,7 +486,7 @@ def bulk_ct4_count_similarity( Computes the pairwise Consonni–Todeschini 4 similarity between count matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column - in output corresponding to `i`-th row from first array and `j`-th row from second array. + in output corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`ct4_count_similarity`. @@ -552,7 +552,7 @@ def _bulk_ct4_count_similarity_two(X: csr_array, Y: csr_array) -> np.ndarray: @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -566,7 +566,7 @@ def bulk_ct4_count_distance( Computes the pairwise Consonni–Todeschini 4 distance between count matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column - in output corresponding to `i`-th row from first array and `j`-th row from second array. + in output corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`ct4_count_distance`. diff --git a/skfp/distances/dice.py b/skfp/distances/dice.py index cc8c584a..3b372eec 100644 --- a/skfp/distances/dice.py +++ b/skfp/distances/dice.py @@ -5,8 +5,8 @@ @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -98,8 +98,8 @@ def dice_binary_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -175,8 +175,8 @@ def dice_binary_distance( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -268,8 +268,8 @@ def dice_count_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -345,7 +345,7 @@ def dice_count_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -359,7 +359,7 @@ def bulk_dice_binary_similarity( Computes the pairwise Dice similarity between binary matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`dice_binary_similarity`. @@ -441,7 +441,7 @@ def _bulk_dice_binary_similarity_two(X: csr_array, Y: csr_array) -> np.ndarray: @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -455,7 +455,7 @@ def bulk_dice_binary_distance( Computes the pairwise Dice distance between binary matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`dice_binary_distance`. @@ -500,7 +500,7 @@ def bulk_dice_binary_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -514,7 +514,7 @@ def bulk_dice_count_similarity( Computes the pairwise Dice similarity between count matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`dice_count_similarity`. @@ -595,7 +595,7 @@ def _bulk_dice_count_similarity_two(X: csr_array, Y: csr_array) -> np.ndarray: @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -609,7 +609,7 @@ def bulk_dice_count_distance( Computes the pairwise Dice distance between count matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`dice_count_distance`. diff --git a/skfp/distances/fraggle.py b/skfp/distances/fraggle.py index 2a928524..e601c15e 100644 --- a/skfp/distances/fraggle.py +++ b/skfp/distances/fraggle.py @@ -111,7 +111,7 @@ def fraggle_distance( dist(a, b) = 1 - sim(a, b) - See also :py:func:`fraggle_binary_similarity`. + See also :py:func:`fraggle_similarity`. The calculated distance falls within the range :math:`[0, 1]`. Passing all-zero vectors to this function results in a distance of 0. Note that this measure is asymmetric, and order of query and reference molecules diff --git a/skfp/distances/harris_lahey.py b/skfp/distances/harris_lahey.py index 56966a44..d4c884dd 100644 --- a/skfp/distances/harris_lahey.py +++ b/skfp/distances/harris_lahey.py @@ -5,8 +5,8 @@ @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -141,8 +141,8 @@ def harris_lahey_binary_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -161,7 +161,7 @@ def harris_lahey_binary_distance( dist(a, b) = 1 - sim(a, b) See also :py:func:`harris_lahey_binary_similarity`. It uses the normalized - similarity, scaled to range `[0, 1]`. + similarity, scaled to range ``[0, 1]``. The calculated distance falls within the range :math:`[0, 1]`. Passing all-zero or all-ones vectors to this function results in a distance of 0. @@ -218,7 +218,7 @@ def harris_lahey_binary_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -234,7 +234,7 @@ def bulk_harris_lahey_binary_similarity( Computes the pairwise Harris-Lahey similarity between binary matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`harris_lahey_binary_similarity`. @@ -354,7 +354,7 @@ def _bulk_harris_lahey_binary_similarity_two_sparse( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -368,7 +368,7 @@ def bulk_harris_lahey_binary_distance( Computes the pairwise Harris-Lahey distance between binary matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`harris_lahey_binary_distance`. diff --git a/skfp/distances/kulczynski.py b/skfp/distances/kulczynski.py index d486196b..b59bb9e8 100644 --- a/skfp/distances/kulczynski.py +++ b/skfp/distances/kulczynski.py @@ -5,8 +5,8 @@ @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -117,8 +117,8 @@ def kulczynski_binary_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -190,7 +190,7 @@ def kulczynski_binary_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -204,7 +204,7 @@ def bulk_kulczynski_binary_similarity( Computes the pairwise Kulczynski similarity between binary matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`kulczynski_binary_similarity`. @@ -299,7 +299,7 @@ def _bulk_kulczynski_binary_similarity_two(X: csr_array, Y: csr_array) -> np.nda @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -313,7 +313,7 @@ def bulk_kulczynski_binary_distance( Computes the pairwise Kulczynski distance between binary matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`kulczynski_binary_distance`. diff --git a/skfp/distances/mcconnaughey.py b/skfp/distances/mcconnaughey.py index 2c9333f5..ec8aa10d 100644 --- a/skfp/distances/mcconnaughey.py +++ b/skfp/distances/mcconnaughey.py @@ -5,8 +5,8 @@ @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -117,8 +117,8 @@ def mcconnaughey_binary_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -137,7 +137,7 @@ def mcconnaughey_binary_distance( dist(a, b) = 1 - sim(a, b) See also :py:func:`mcconnaughey_binary_similarity`. It uses the normalized - similarity, scaled to range `[0, 1]`. + similarity, scaled to range ``[0, 1]``. The calculated distance falls within the range :math:`[0, 1]`. Passing all-zero vectors to this function results in a distance of 0. @@ -191,7 +191,7 @@ def mcconnaughey_binary_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -207,7 +207,7 @@ def bulk_mcconnaughey_binary_similarity( Computes the pairwise McConnaughey similarity between binary matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`mcconnaughey_binary_similarity`. @@ -313,7 +313,7 @@ def _bulk_mcconnaughey_binary_similarity_two( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -327,7 +327,7 @@ def bulk_mcconnaughey_binary_distance( Computes the pairwise McConnaughey distance between binary matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`mcconnaughey_binary_distance`. diff --git a/skfp/distances/rand.py b/skfp/distances/rand.py index 698b4b11..a34d9343 100644 --- a/skfp/distances/rand.py +++ b/skfp/distances/rand.py @@ -5,8 +5,8 @@ @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -103,8 +103,8 @@ def rand_binary_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -176,7 +176,7 @@ def rand_binary_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -190,8 +190,8 @@ def bulk_rand_binary_similarity( Computes the pairwise Rand (also known as All-Bit or Sokal-Michener) similarity between binary matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with - `i`-th row and `j`-th column in output corresponding to `i`-th row from first array - and `j`-th row from second array. + `i`-th row and `j`-th column in output corresponding to `i`-th row from the first array + and `j`-th row from the second array. See also :py:func:`rand_binary_similarity`. @@ -269,7 +269,7 @@ def _bulk_rand_binary_similarity_two(X: csr_array, Y: csr_array) -> np.ndarray: @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -283,8 +283,8 @@ def bulk_rand_binary_distance( Computes the pairwise Rand distance between binary matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th - column in output corresponding to `i`-th row from first array and `j`-th - row from second array. + column in output corresponding to `i`-th row from the first array and `j`-th + row from the second array. See also :py:func:`rand_binary_distance`. diff --git a/skfp/distances/rogot_goldberg.py b/skfp/distances/rogot_goldberg.py index ad5f7681..e5b58771 100644 --- a/skfp/distances/rogot_goldberg.py +++ b/skfp/distances/rogot_goldberg.py @@ -5,8 +5,8 @@ @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -120,8 +120,8 @@ def rogot_goldberg_binary_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -193,7 +193,7 @@ def rogot_goldberg_binary_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -208,7 +208,7 @@ def bulk_rogot_goldberg_binary_similarity( Computes the pairwise Rogot-Goldberg similarity between binary matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`rogot_goldberg_binary_similarity`. @@ -318,7 +318,7 @@ def _bulk_rogot_goldberg_binary_similarity_two( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -332,7 +332,7 @@ def bulk_rogot_goldberg_binary_distance( Computes the pairwise Rogot-Goldberg distance between binary matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`rogot_goldberg_binary_distance`. diff --git a/skfp/distances/russell.py b/skfp/distances/russell.py index 639a1a78..7832fec2 100644 --- a/skfp/distances/russell.py +++ b/skfp/distances/russell.py @@ -5,8 +5,8 @@ @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -97,8 +97,8 @@ def russell_binary_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -170,7 +170,7 @@ def russell_binary_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -185,7 +185,7 @@ def bulk_russell_binary_similarity( Computes the pairwise Russell similarity between binary matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`russell_binary_similarity`. @@ -231,7 +231,7 @@ def _bulk_russell_binary_similarity_two(X: csr_array, Y: csr_array) -> np.ndarra @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -245,17 +245,17 @@ def bulk_russell_binary_distance( Computes the pairwise Russell distance between binary matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`russell_binary_distance`. Parameters ---------- X : ndarray - First binary input array, of shape :math:`m \times m` + First binary input array, of shape :math:`m \times d` Y : ndarray, default=None - Second binary input array, of shape :math:`n \times n`. If not passed, distances + Second binary input array, of shape :math:`n \times d`. If not passed, distances are computed between rows of X. Returns diff --git a/skfp/distances/simpson.py b/skfp/distances/simpson.py index ba30c99c..23c71007 100644 --- a/skfp/distances/simpson.py +++ b/skfp/distances/simpson.py @@ -5,8 +5,8 @@ @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -95,8 +95,8 @@ def simpson_binary_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -171,7 +171,7 @@ def simpson_binary_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -186,7 +186,7 @@ def bulk_simpson_binary_similarity( coefficient) similarity between binary matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`simpson_binary_similarity`. @@ -262,7 +262,7 @@ def _bulk_simpson_binary_similarity_two(X: csr_array, Y: csr_array) -> np.ndarra @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -276,7 +276,7 @@ def bulk_simpson_binary_distance( Computes the pairwise Simpson distance between binary matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`simpson_binary_distance`. diff --git a/skfp/distances/sokal_sneath.py b/skfp/distances/sokal_sneath.py index 3fbc0cca..2d13d751 100644 --- a/skfp/distances/sokal_sneath.py +++ b/skfp/distances/sokal_sneath.py @@ -5,8 +5,8 @@ @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -102,8 +102,8 @@ def sokal_sneath_2_binary_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -175,7 +175,7 @@ def sokal_sneath_2_binary_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -281,7 +281,7 @@ def _bulk_sokal_sneath_2_binary_similarity_two( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -295,8 +295,8 @@ def bulk_sokal_sneath_2_binary_distance( Computes the pairwise Sokal-Sneath distance 2 between binary matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th - column in output corresponding to `i`-th row from first array and `j`-th row - from second array. + column in output corresponding to `i`-th row from the first array and `j`-th row + from the second array. See also :py:func:`sokal_sneath_2_binary_distance`. diff --git a/skfp/distances/tanimoto.py b/skfp/distances/tanimoto.py index 84244751..47e09433 100644 --- a/skfp/distances/tanimoto.py +++ b/skfp/distances/tanimoto.py @@ -5,8 +5,8 @@ @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -85,8 +85,8 @@ def tanimoto_binary_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -151,8 +151,8 @@ def tanimoto_binary_distance( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -234,8 +234,8 @@ def tanimoto_count_similarity( @validate_params( { - "vec_a": ["array-like", csr_array], - "vec_b": ["array-like", csr_array], + "vec_a": ["array-like", "sparse matrix"], + "vec_b": ["array-like", "sparse matrix"], }, prefer_skip_nested_validation=True, ) @@ -299,7 +299,7 @@ def tanimoto_count_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -313,17 +313,17 @@ def bulk_tanimoto_binary_similarity( Computes the pairwise Tanimoto similarity between binary matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`tanimoto_binary_similarity`. Parameters ---------- X : ndarray or CSR sparse array - First binary input array or sparse matrix, of shape :math:`m \times m`. + First binary input array or sparse matrix, of shape :math:`m \times d`. Y : ndarray or CSR sparse array, default=None - Second binary input array or sparse matrix, of shape :math:`n \times n`. + Second binary input array or sparse matrix, of shape :math:`n \times d`. If not passed, similarities are computed between rows of X. Returns @@ -390,7 +390,7 @@ def _bulk_tanimoto_binary_similarity_two(X: csr_array, Y: csr_array) -> np.ndarr @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -404,17 +404,17 @@ def bulk_tanimoto_binary_distance( Computes the pairwise Tanimoto distance between binary matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`tanimoto_binary_distance`. Parameters ---------- X : ndarray or CSR sparse array - First binary input array or sparse matrix, of shape :math:`m \times m` + First binary input array or sparse matrix, of shape :math:`m \times d` Y : ndarray or CSR sparse array, default=None - Second binary input array or sparse matrix, of shape :math:`n \times n`. + Second binary input array or sparse matrix, of shape :math:`n \times d`. If not passed, distances are computed between rows of X. Returns @@ -449,7 +449,7 @@ def bulk_tanimoto_binary_distance( @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -463,17 +463,17 @@ def bulk_tanimoto_count_similarity( Computes the pairwise Tanimoto similarity between count matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`tanimoto_count_similarity`. Parameters ---------- X : ndarray or CSR sparse array - First binary input array or sparse matrix, of shape :math:`m \times m` + First binary input array or sparse matrix, of shape :math:`m \times d` Y : ndarray or CSR sparse array, default=None - Second binary input array or sparse matrix, of shape :math:`n \times n`. + Second binary input array or sparse matrix, of shape :math:`n \times d`. If not passed, similarities are computed between rows of X. Returns @@ -540,7 +540,7 @@ def _bulk_tanimoto_count_similarity_two(X: csr_array, Y: csr_array) -> np.ndarra @validate_params( { - "X": ["array-like", csr_array], + "X": ["array-like", "sparse matrix"], "Y": ["array-like", csr_array, None], }, prefer_skip_nested_validation=True, @@ -554,17 +554,17 @@ def bulk_tanimoto_count_distance( Computes the pairwise Tanimoto distance between count matrices. If one array is passed, distances are computed between its rows. For two arrays, distances are between their respective rows, with `i`-th row and `j`-th column in output - corresponding to `i`-th row from first array and `j`-th row from second array. + corresponding to `i`-th row from the first array and `j`-th row from the second array. See also :py:func:`tanimoto_count_distance`. Parameters ---------- X : ndarray or CSR sparse array - First binary input array or sparse matrix, of shape :math:`m \times m` + First binary input array or sparse matrix, of shape :math:`m \times d` Y : ndarray or CSR sparse array, default=None - Second binary input array or sparse matrix, of shape :math:`n \times n`. + Second binary input array or sparse matrix, of shape :math:`n \times d`. If not passed, distances are computed between rows of X. Returns diff --git a/skfp/filters/beyond_ro5.py b/skfp/filters/beyond_ro5.py index a96e98ee..dff3e7ff 100644 --- a/skfp/filters/beyond_ro5.py +++ b/skfp/filters/beyond_ro5.py @@ -22,7 +22,7 @@ class BeyondRo5Filter(BaseFilter): Molecule can violate at most one of the rules (conditions): - - molecular weight <= 1000 daltons + - molecular weight <= 1000 - logP in range [-2, 10] - HBA <= 15 - HBD <= 6 diff --git a/skfp/filters/faf4_druglike.py b/skfp/filters/faf4_druglike.py index ec2ebb96..7fcead51 100644 --- a/skfp/filters/faf4_druglike.py +++ b/skfp/filters/faf4_druglike.py @@ -91,7 +91,7 @@ class FAF4DruglikeFilter(BaseFilter): Number of inputs processed in each batch. ``None`` divides input data into equal-sized parts, as many as ``n_jobs``. - verbose : int, default=0 + verbose : int or dict, default=0 Controls the verbosity when filtering molecules. If a dictionary is passed, it is treated as kwargs for ``tqdm()``, and can be used to control the progress bar. diff --git a/skfp/filters/faf4_leadlike.py b/skfp/filters/faf4_leadlike.py index b0eb3dc7..8d2d30e6 100644 --- a/skfp/filters/faf4_leadlike.py +++ b/skfp/filters/faf4_leadlike.py @@ -92,8 +92,10 @@ class FAF4LeadlikeFilter(BaseFilter): Number of inputs processed in each batch. ``None`` divides input data into equal-sized parts, as many as ``n_jobs``. - verbose : int, default=0 + verbose : int or dict, default=0 Controls the verbosity when filtering molecules. + If a dictionary is passed, it is treated as kwargs for ``tqdm()``, + and can be used to control the progress bar. References ---------- diff --git a/skfp/filters/gsk.py b/skfp/filters/gsk.py index 61c6d7a2..5aff2252 100644 --- a/skfp/filters/gsk.py +++ b/skfp/filters/gsk.py @@ -14,7 +14,7 @@ class GSKFilter(BaseFilter): Molecule must fulfill conditions: - - molecular weight <= 400 daltons + - molecular weight <= 400 - logP <= 4 Parameters @@ -52,8 +52,10 @@ class GSKFilter(BaseFilter): Number of inputs processed in each batch. ``None`` divides input data into equal-sized parts, as many as ``n_jobs``. - verbose : int, default=0 + verbose : int or dict, default=0 Controls the verbosity when filtering molecules. + If a dictionary is passed, it is treated as kwargs for ``tqdm()``, + and can be used to control the progress bar. References ---------- diff --git a/skfp/filters/nibr.py b/skfp/filters/nibr.py index ad78d45c..127f7e30 100644 --- a/skfp/filters/nibr.py +++ b/skfp/filters/nibr.py @@ -61,8 +61,10 @@ class NIBRFilter(BaseFilter): Number of inputs processed in each batch. ``None`` divides input data into equal-sized parts, as many as ``n_jobs``. - verbose : int, default=0 + verbose : int or dict, default=0 Controls the verbosity when filtering molecules. + If a dictionary is passed, it is treated as kwargs for ``tqdm()``, + and can be used to control the progress bar. References ---------- diff --git a/skfp/filters/oprea.py b/skfp/filters/oprea.py index 52190328..c6a99c75 100644 --- a/skfp/filters/oprea.py +++ b/skfp/filters/oprea.py @@ -59,8 +59,10 @@ class OpreaFilter(BaseFilter): Number of inputs processed in each batch. ``None`` divides input data into equal-sized parts, as many as ``n_jobs``. - verbose : int, default=0 + verbose : int or dict, default=0 Controls the verbosity when filtering molecules. + If a dictionary is passed, it is treated as kwargs for ``tqdm()``, + and can be used to control the progress bar. References ---------- diff --git a/skfp/filters/rule_of_three.py b/skfp/filters/rule_of_three.py index 17504a6c..72d942a2 100644 --- a/skfp/filters/rule_of_three.py +++ b/skfp/filters/rule_of_three.py @@ -15,7 +15,7 @@ class RuleOfThreeFilter(BaseFilter): """ Rule of three (Ro3). - Rule optimised to search for fragment-based lead-like compounds with desired properties. + Rule optimized to search for fragment-based lead-like compounds with desired properties. It was described in [1]_. Molecule must fulfill conditions: diff --git a/skfp/filters/rule_of_two.py b/skfp/filters/rule_of_two.py index e1358da0..b6f393cf 100644 --- a/skfp/filters/rule_of_two.py +++ b/skfp/filters/rule_of_two.py @@ -27,11 +27,14 @@ class RuleOfTwoFilter(BaseFilter): filter less restrictive. return_type : {"mol", "indicators", "condition_indicators"}, default="mol" - What values to return as the filtering result. "mol" returns list of - molecules passing the filter. "indicators" returns a binary vector with - indicators which molecules pass the filter. "condition_indicators" returns - a Pandas DataFrame with molecules in rows, filter conditions in columns, and - 0/1 indicators whether a given condition was fulfilled by a given molecule. + What values to return as the filtering result. + + - ``"mol"`` - return a list of molecules remaining in the dataset after filtering + - ``"indicators"`` - return a binary vector with indicators which molecules pass + the filter (1) and which would be removed (0) + - ``"condition_indicators"`` - return a Pandas DataFrame with molecules in rows, + filter conditions in columns, and 0/1 indicators whether a given condition was + fulfilled by a given molecule return_indicators : bool, default=False Whether to return a binary vector with indicators which molecules pass the diff --git a/skfp/filters/rule_of_veber.py b/skfp/filters/rule_of_veber.py index 4becba25..c6acd1d5 100644 --- a/skfp/filters/rule_of_veber.py +++ b/skfp/filters/rule_of_veber.py @@ -24,11 +24,14 @@ class RuleOfVeberFilter(BaseFilter): filter less restrictive. return_type : {"mol", "indicators", "condition_indicators"}, default="mol" - What values to return as the filtering result. "mol" returns list of - molecules passing the filter. "indicators" returns a binary vector with - indicators which molecules pass the filter. "condition_indicators" returns - a Pandas DataFrame with molecules in rows, filter conditions in columns, and - 0/1 indicators whether a given condition was fulfilled by a given molecule. + What values to return as the filtering result. + + - ``"mol"`` - return a list of molecules remaining in the dataset after filtering + - ``"indicators"`` - return a binary vector with indicators which molecules pass + the filter (1) and which would be removed (0) + - ``"condition_indicators"`` - return a Pandas DataFrame with molecules in rows, + filter conditions in columns, and 0/1 indicators whether a given condition was + fulfilled by a given molecule return_indicators : bool, default=False Whether to return a binary vector with indicators which molecules pass the diff --git a/skfp/filters/rule_of_xu.py b/skfp/filters/rule_of_xu.py index 9d7320dc..0b9021d6 100644 --- a/skfp/filters/rule_of_xu.py +++ b/skfp/filters/rule_of_xu.py @@ -25,11 +25,14 @@ class RuleOfXuFilter(BaseFilter): filter less restrictive. return_type : {"mol", "indicators", "condition_indicators"}, default="mol" - What values to return as the filtering result. "mol" returns list of - molecules passing the filter. "indicators" returns a binary vector with - indicators which molecules pass the filter. "condition_indicators" returns - a Pandas DataFrame with molecules in rows, filter conditions in columns, and - 0/1 indicators whether a given condition was fulfilled by a given molecule. + What values to return as the filtering result. + + - ``"mol"`` - return a list of molecules remaining in the dataset after filtering + - ``"indicators"`` - return a binary vector with indicators which molecules pass + the filter (1) and which would be removed (0) + - ``"condition_indicators"`` - return a Pandas DataFrame with molecules in rows, + filter conditions in columns, and 0/1 indicators whether a given condition was + fulfilled by a given molecule return_indicators : bool, default=False Whether to return a binary vector with indicators which molecules pass the diff --git a/skfp/filters/tice_herbicides.py b/skfp/filters/tice_herbicides.py index 24e43507..2bd8aae2 100644 --- a/skfp/filters/tice_herbicides.py +++ b/skfp/filters/tice_herbicides.py @@ -29,11 +29,14 @@ class TiceHerbicidesFilter(BaseFilter): filter less restrictive. return_type : {"mol", "indicators", "condition_indicators"}, default="mol" - What values to return as the filtering result. "mol" returns list of - molecules passing the filter. "indicators" returns a binary vector with - indicators which molecules pass the filter. "condition_indicators" returns - a NumPy array with molecules in rows, filter conditions in columns, and - 0/1 indicators whether a given condition was fulfilled by a given molecule. + What values to return as the filtering result. + + - ``"mol"`` - return a list of molecules remaining in the dataset after filtering + - ``"indicators"`` - return a binary vector with indicators which molecules pass + the filter (1) and which would be removed (0) + - ``"condition_indicators"`` - return a Pandas DataFrame with molecules in rows, + filter conditions in columns, and 0/1 indicators whether a given condition was + fulfilled by a given molecule return_indicators : bool, default=False Whether to return a binary vector with indicators which molecules pass the @@ -82,8 +85,8 @@ class TiceHerbicidesFilter(BaseFilter): def __init__( self, allow_one_violation: bool = False, - return_indicators: bool = False, return_type: str = "mol", + return_indicators: bool = False, n_jobs: int | None = None, batch_size: int | None = None, verbose: int | dict = 0, @@ -98,8 +101,8 @@ def __init__( super().__init__( condition_names=condition_names, allow_one_violation=allow_one_violation, - return_indicators=return_indicators, return_type=return_type, + return_indicators=return_indicators, n_jobs=n_jobs, batch_size=batch_size, verbose=verbose, diff --git a/skfp/filters/tice_insecticides.py b/skfp/filters/tice_insecticides.py index 433d1bcb..37cdbf6c 100644 --- a/skfp/filters/tice_insecticides.py +++ b/skfp/filters/tice_insecticides.py @@ -29,11 +29,14 @@ class TiceInsecticidesFilter(BaseFilter): filter less restrictive. return_type : {"mol", "indicators", "condition_indicators"}, default="mol" - What values to return as the filtering result. "mol" returns list of - molecules passing the filter. "indicators" returns a binary vector with - indicators which molecules pass the filter. "condition_indicators" returns - a NumPy array with molecules in rows, filter conditions in columns, and - 0/1 indicators whether a given condition was fulfilled by a given molecule. + What values to return as the filtering result. + + - ``"mol"`` - return a list of molecules remaining in the dataset after filtering + - ``"indicators"`` - return a binary vector with indicators which molecules pass + the filter (1) and which would be removed (0) + - ``"condition_indicators"`` - return a Pandas DataFrame with molecules in rows, + filter conditions in columns, and 0/1 indicators whether a given condition was + fulfilled by a given molecule return_indicators : bool, default=False Whether to return a binary vector with indicators which molecules pass the @@ -82,8 +85,8 @@ class TiceInsecticidesFilter(BaseFilter): def __init__( self, allow_one_violation: bool = False, - return_indicators: bool = False, return_type: str = "mol", + return_indicators: bool = False, n_jobs: int | None = None, batch_size: int | None = None, verbose: int | dict = 0, @@ -98,8 +101,8 @@ def __init__( super().__init__( condition_names=condition_names, allow_one_violation=allow_one_violation, - return_indicators=return_indicators, return_type=return_type, + return_indicators=return_indicators, n_jobs=n_jobs, batch_size=batch_size, verbose=verbose, diff --git a/skfp/filters/valence_discovery.py b/skfp/filters/valence_discovery.py index 7ad78616..3d782c1e 100644 --- a/skfp/filters/valence_discovery.py +++ b/skfp/filters/valence_discovery.py @@ -58,11 +58,14 @@ class ValenceDiscoveryFilter(BaseFilter): filter less restrictive. return_type : {"mol", "indicators", "condition_indicators"}, default="mol" - What values to return as the filtering result. "mol" returns list of - molecules passing the filter. "indicators" returns a binary vector with - indicators which molecules pass the filter. "condition_indicators" returns - a NumPy array with molecules in rows, filter conditions in columns, and - 0/1 indicators whether a given condition was fulfilled by a given molecule. + What values to return as the filtering result. + + - ``"mol"`` - return a list of molecules remaining in the dataset after filtering + - ``"indicators"`` - return a binary vector with indicators which molecules pass + the filter (1) and which would be removed (0) + - ``"condition_indicators"`` - return a Pandas DataFrame with molecules in rows, + filter conditions in columns, and 0/1 indicators whether a given condition was + fulfilled by a given molecule return_indicators : bool, default=False Whether to return a binary vector with indicators which molecules pass the @@ -83,8 +86,10 @@ class ValenceDiscoveryFilter(BaseFilter): Number of inputs processed in each batch. ``None`` divides input data into equal-sized parts, as many as ``n_jobs``. - verbose : int, default=0 + verbose : int or dict, default=0 Controls the verbosity when filtering molecules. + If a dictionary is passed, it is treated as kwargs for ``tqdm()``, + and can be used to control the progress bar. References ---------- @@ -107,8 +112,8 @@ class ValenceDiscoveryFilter(BaseFilter): def __init__( self, allow_one_violation: bool = False, - return_indicators: bool = False, return_type: str = "mol", + return_indicators: bool = False, n_jobs: int | None = None, batch_size: int | None = None, verbose: int = 0, @@ -134,8 +139,8 @@ def __init__( super().__init__( condition_names=condition_names, allow_one_violation=allow_one_violation, - return_indicators=return_indicators, return_type=return_type, + return_indicators=return_indicators, n_jobs=n_jobs, batch_size=batch_size, verbose=verbose, diff --git a/skfp/filters/zinc_druglike.py b/skfp/filters/zinc_druglike.py index 34cf3746..d3c94f30 100644 --- a/skfp/filters/zinc_druglike.py +++ b/skfp/filters/zinc_druglike.py @@ -52,20 +52,23 @@ class ZINCDruglikeFilter(BaseFilter): filter less restrictive. return_type : {"mol", "indicators", "condition_indicators"}, default="mol" - What values to return as the filtering result. "mol" returns list of - molecules passing the filter. "indicators" returns a binary vector with - indicators which molecules pass the filter. "condition_indicators" returns - a Pandas DataFrame with molecules in rows, filter conditions in columns, and - 0/1 indicators whether a given condition was fulfilled by a given molecule. + What values to return as the filtering result. + + - ``"mol"`` - return a list of molecules remaining in the dataset after filtering + - ``"indicators"`` - return a binary vector with indicators which molecules pass + the filter (1) and which would be removed (0) + - ``"condition_indicators"`` - return a Pandas DataFrame with molecules in rows, + filter conditions in columns, and 0/1 indicators whether a given condition was + fulfilled by a given molecule return_indicators : bool, default=False Whether to return a binary vector with indicators which molecules pass the filter, instead of list of molecules. .. deprecated:: 1.17 - return_indicators is deprecated and will be removed in version 2.0. - Use return_type instead. If return_indicators is set to True, - it will take precedence over return_type. + ``return_indicators`` is deprecated and will be removed in version 2.0. + Use ``return_type`` instead. If ``return_indicators`` is set to ``True``, + it will take precedence over ``return_type``. n_jobs : int, default=None The number of jobs to run in parallel. :meth:`transform_x_y` and @@ -77,8 +80,10 @@ class ZINCDruglikeFilter(BaseFilter): Number of inputs processed in each batch. ``None`` divides input data into equal-sized parts, as many as ``n_jobs``. - verbose : int, default=0 + verbose : int or dict, default=0 Controls the verbosity when filtering molecules. + If a dictionary is passed, it is treated as kwargs for ``tqdm()``, + and can be used to control the progress bar. References ---------- diff --git a/skfp/fingerprints/bcut2d.py b/skfp/fingerprints/bcut2d.py index ec2b8727..28cd34c7 100644 --- a/skfp/fingerprints/bcut2d.py +++ b/skfp/fingerprints/bcut2d.py @@ -172,9 +172,7 @@ def get_feature_names_out(self, input_features=None) -> np.ndarray: # noqa: ARG ] return np.asarray(feature_names, dtype=object) - def transform( - self, X: Sequence[str | Mol], copy: bool = False - ) -> np.ndarray | csr_array: + def transform(self, X: Sequence[Mol], copy: bool = False) -> np.ndarray | csr_array: """ Compute BCUT2D fingerprints. diff --git a/skfp/fingerprints/e3fp_fp.py b/skfp/fingerprints/e3fp_fp.py index a69e1d96..c14395c5 100644 --- a/skfp/fingerprints/e3fp_fp.py +++ b/skfp/fingerprints/e3fp_fp.py @@ -178,9 +178,7 @@ def _validate_params(self) -> None: f"fp_size={self.fp_size}" ) - def transform( - self, X: Sequence[str | Mol], copy: bool = False - ) -> np.ndarray | csr_array: + def transform(self, X: Sequence[Mol], copy: bool = False) -> np.ndarray | csr_array: """ Compute E3FP fingerprints. diff --git a/skfp/fingerprints/electroshape.py b/skfp/fingerprints/electroshape.py index 749f8ada..bf2a1358 100644 --- a/skfp/fingerprints/electroshape.py +++ b/skfp/fingerprints/electroshape.py @@ -52,9 +52,9 @@ class ElectroShapeFingerprint(BaseFingerprintTransformer): charge_errors : {"raise", "ignore", "zero"}, default="raise" How to handle errors during calculation of atomic partial charges. ``"raise"`` - immediately raises any errors. ``"NaN"`` ignores any atoms that failed the + immediately raises any errors. ``"ignore"`` ignores any atoms that failed the computation; note that if all atoms fail, the error will be raised (use - ``errors`` parameter to control this). ``"zero"`` uses default value of 0 to + ``errors`` parameter to control this). ``"zero"`` uses the default value of 0 to fill all problematic charges. errors : {"raise", "NaN", "ignore"}, default="raise" @@ -185,9 +185,7 @@ def get_feature_names_out(self, input_features=None) -> np.ndarray: # noqa: ARG ] return np.asarray(feature_names, dtype=object) - def transform( - self, X: Sequence[str | Mol], copy: bool = False - ) -> np.ndarray | csr_array: + def transform(self, X: Sequence[Mol], copy: bool = False) -> np.ndarray | csr_array: """ Compute ElectroShape fingerprints. diff --git a/skfp/fingerprints/getaway.py b/skfp/fingerprints/getaway.py index 03de9c92..eb30d4b3 100644 --- a/skfp/fingerprints/getaway.py +++ b/skfp/fingerprints/getaway.py @@ -218,9 +218,7 @@ def get_feature_names_out(self, input_features=None) -> np.ndarray: # noqa: ARG return np.asarray(feature_names, dtype=object) - def transform( - self, X: Sequence[str | Mol], copy: bool = False - ) -> np.ndarray | csr_array: + def transform(self, X: Sequence[Mol], copy: bool = False) -> np.ndarray | csr_array: """ Compute GETAWAY fingerprints. diff --git a/skfp/fingerprints/rdf.py b/skfp/fingerprints/rdf.py index 7a6bc8a3..aff8f437 100644 --- a/skfp/fingerprints/rdf.py +++ b/skfp/fingerprints/rdf.py @@ -164,9 +164,7 @@ def get_feature_names_out(self, input_features=None) -> np.ndarray: # noqa: ARG ] return np.asarray(feature_names, dtype=object) - def transform( - self, X: Sequence[str | Mol], copy: bool = False - ) -> np.ndarray | csr_array: + def transform(self, X: Sequence[Mol], copy: bool = False) -> np.ndarray | csr_array: """ Compute RDF fingerprints. diff --git a/skfp/fingerprints/rdkit_2d_desc.py b/skfp/fingerprints/rdkit_2d_desc.py index eda5ac14..254acb49 100644 --- a/skfp/fingerprints/rdkit_2d_desc.py +++ b/skfp/fingerprints/rdkit_2d_desc.py @@ -116,7 +116,7 @@ def __init__( self.normalized = normalized self.clip_val = clip_val - def get_feature_names_out(self, input_features=None): # noqa: ARG002 + def get_feature_names_out(self, input_features=None) -> np.ndarray: # noqa: ARG002 # noqa: ARG002 """ Get fingerprint output feature names. They correspond to RDKit function names for computing descriptors. diff --git a/skfp/fingerprints/usr.py b/skfp/fingerprints/usr.py index a2cdb103..2084ecec 100644 --- a/skfp/fingerprints/usr.py +++ b/skfp/fingerprints/usr.py @@ -71,7 +71,7 @@ class USRFingerprint(BaseFingerprintTransformer): :class:`USRCAT` : Related fingerprint, which additionally uses pharmacophoric atom types. - :class:`ElectroShaoe` : Related fingerprint, which additionally uses atomic + :class:`ElectroShape` : Related fingerprint, which additionally uses atomic partial charges. References @@ -156,9 +156,7 @@ def get_feature_names_out(self, input_features=None) -> np.ndarray: # noqa: ARG ] return np.asarray(feature_names, dtype=object) - def transform( - self, X: Sequence[str | Mol], copy: bool = False - ) -> np.ndarray | csr_array: + def transform(self, X: Sequence[Mol], copy: bool = False) -> np.ndarray | csr_array: """ Compute USR fingerprints. If ``errors`` is set to ``"ignore"``, then in case of errors less than n_samples values may be returned. diff --git a/skfp/fingerprints/usrcat.py b/skfp/fingerprints/usrcat.py index b6c23cca..c2c40884 100644 --- a/skfp/fingerprints/usrcat.py +++ b/skfp/fingerprints/usrcat.py @@ -166,9 +166,7 @@ def get_feature_names_out(self, input_features=None) -> np.ndarray: # noqa: ARG return np.asarray(feature_names, dtype=object) - def transform( - self, X: Sequence[str | Mol], copy: bool = False - ) -> np.ndarray | csr_array: + def transform(self, X: Sequence[Mol], copy: bool = False) -> np.ndarray | csr_array: """ Compute USRCAT fingerprints. If ``errors`` is set to ``"ignore"``, then in case of errors less than n_samples values may be returned. diff --git a/skfp/fingerprints/vsa.py b/skfp/fingerprints/vsa.py index 0bab67d0..1df17272 100644 --- a/skfp/fingerprints/vsa.py +++ b/skfp/fingerprints/vsa.py @@ -151,9 +151,9 @@ def _get_n_features_out(self, variant: str) -> int: try: return n_features_out[variant] except KeyError as err: - raise ValueError(f'Variant "{variant}" not recognized"') from err + raise ValueError(f'Variant "{variant}" not recognized') from err - def get_feature_names_out(self, input_features=None) -> np.ndarray: # noqa: ARG002 + def get_feature_names_out(self, input_features=None) -> np.ndarray: # noqa: ARG002 # noqa: ARG002 """ Get fingerprint output feature names. They correspond to histogram bins for descriptors. diff --git a/skfp/fingerprints/whim.py b/skfp/fingerprints/whim.py index 749e987d..6ff74297 100644 --- a/skfp/fingerprints/whim.py +++ b/skfp/fingerprints/whim.py @@ -140,7 +140,7 @@ def __init__( ) self.clip_val = clip_val - def get_feature_names_out(self, input_features=None) -> np.ndarray: # noqa: ARG002 + def get_feature_names_out(self, input_features=None) -> np.ndarray: # noqa: ARG002 # noqa: ARG002 """ Get fingerprint output feature names. They correspond to various descriptors derived from weighted covariance matrix. See references @@ -215,9 +215,7 @@ def get_feature_names_out(self, input_features=None) -> np.ndarray: # noqa: ARG return np.asarray(feature_names, dtype=object) - def transform( - self, X: Sequence[str | Mol], copy: bool = False - ) -> np.ndarray | csr_array: + def transform(self, X: Sequence[Mol], copy: bool = False) -> np.ndarray | csr_array: """ Compute WHIM fingerprints. diff --git a/skfp/metrics/auroc.py b/skfp/metrics/auroc.py index b1ca9de2..c09db4df 100644 --- a/skfp/metrics/auroc.py +++ b/skfp/metrics/auroc.py @@ -14,12 +14,12 @@ @deprecated( "Deprecated for scikit-learn >1.6, which returns np.nan for constant targets. " - "Will be removed in scikit-fingerprints 1.15" + "Will be removed in scikit-fingerprints 2.0" ) @validate_params( { "y_true": ["array-like"], - "y_pred": ["array-like"], + "y_score": ["array-like"], "constant_target_behavior": [ StrOptions({"raise"}), "nan", @@ -51,7 +51,7 @@ def auroc_score( y_score : array-like of shape (n_samples,) or (n_samples, n_outputs) Target scores, i.e. probability of the class with the greater label for each - output** of the classifier. + output of the classifier. *args, **kwargs Any additional parameters for the underlying ``roc_auc_score`` function. diff --git a/skfp/metrics/multioutput.py b/skfp/metrics/multioutput.py index 0f9757d2..5cafcf99 100644 --- a/skfp/metrics/multioutput.py +++ b/skfp/metrics/multioutput.py @@ -56,7 +56,7 @@ def multioutput_accuracy_score( y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. @@ -127,9 +127,9 @@ def multioutput_auroc_score( y_score : array-like of shape (n_samples,) or (n_samples, n_outputs) Target scores, i.e. probability of the class with the greater label for each - output** of the classifier. + output of the classifier. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. @@ -197,9 +197,9 @@ def multioutput_auprc_score( y_score : array-like of shape (n_samples,) or (n_samples, n_outputs) Target scores, i.e. probability of the class with the greater label for each - output** of the classifier. + output of the classifier. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. @@ -267,7 +267,7 @@ def multioutput_balanced_accuracy_score( y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. @@ -335,7 +335,7 @@ def multioutput_cohen_kappa_score( y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. @@ -405,7 +405,7 @@ def multioutput_f1_score( y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. @@ -468,7 +468,7 @@ def multioutput_matthews_corr_coef( y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. @@ -536,7 +536,7 @@ def multioutput_mean_absolute_error( y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. @@ -604,7 +604,7 @@ def multioutput_mean_squared_error( y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. @@ -673,7 +673,7 @@ def multioutput_precision_score( y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. @@ -742,7 +742,7 @@ def multioutput_recall_score( y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. @@ -810,7 +810,7 @@ def multioutput_root_mean_squared_error( y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. @@ -876,7 +876,7 @@ def multioutput_spearman_correlation( y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. - suppress_warnings : boolean, default=False + suppress_warnings : bool, default=False Whether to suppress any warnings that may arise during evaluation on some tasks. diff --git a/skfp/metrics/utils.py b/skfp/metrics/utils.py index c0092ee2..88d304fe 100644 --- a/skfp/metrics/utils.py +++ b/skfp/metrics/utils.py @@ -10,7 +10,7 @@ def extract_pos_proba(predictions: np.ndarray | list[np.ndarray]) -> np.ndarray: """ Extract positive class probabilities (``y-score``). - Probabilitic metrics like AUROC or AUPRC use predicted probabilities. This + Probabilistic metrics like AUROC or AUPRC use predicted probabilities. This function extracts them from ``.predict_proba()`` results. Returns `(n_samples,)` shape for single-task, and `(n_samples, n_tasks)` for @@ -25,7 +25,7 @@ def extract_pos_proba(predictions: np.ndarray | list[np.ndarray]) -> np.ndarray: Returns ------- - y_score : NumPy array of shape (n_samples,) or (n_samples, n_tasks + y_score : NumPy array of shape (n_samples,) or (n_samples, n_tasks) Predicted positive class probabilities for each task. Examples diff --git a/skfp/metrics/virtual_screening.py b/skfp/metrics/virtual_screening.py index d148888f..423f34e9 100644 --- a/skfp/metrics/virtual_screening.py +++ b/skfp/metrics/virtual_screening.py @@ -10,7 +10,7 @@ @validate_params( { "y_true": ["array-like"], - "y_pred": ["array-like"], + "y_score": ["array-like"], "fraction": [Interval(Real, 0, 1, closed="neither")], }, prefer_skip_nested_validation=True, @@ -98,7 +98,7 @@ def enrichment_factor( @validate_params( { "y_true": ["array-like"], - "y_pred": ["array-like"], + "y_score": ["array-like"], "alpha": [Interval(Real, 0, None, closed="neither")], }, prefer_skip_nested_validation=True, @@ -191,7 +191,7 @@ def rie_score( @validate_params( { "y_true": ["array-like"], - "y_pred": ["array-like"], + "y_score": ["array-like"], "alpha": [Interval(Real, 0, None, closed="neither")], }, prefer_skip_nested_validation=True, diff --git a/skfp/preprocessing/conformer_generator.py b/skfp/preprocessing/conformer_generator.py index c631a873..345459c7 100644 --- a/skfp/preprocessing/conformer_generator.py +++ b/skfp/preprocessing/conformer_generator.py @@ -30,10 +30,10 @@ class ConformerGenerator(BasePreprocessor): """ - Generate molecule conformer. + Generate molecule conformers. The implementation uses RDKit and distance geometry (DG) approach [1]_, with - optimized ETKDGv3 improvements for small rings, macrocycles and experimental + optimized ETKDGv3 improvements for small rings, macrocycles, and experimental torsional angle preferences [2]_. Generated conformations are optionally optimized with a force field approach. @@ -78,7 +78,7 @@ class ConformerGenerator(BasePreprocessor): How to select final conformer for each molecule when multiple conformers are generated. "first" selects first conformer generated by RDKit. - suppress_warnings: bool, default=False + suppress_warnings : bool, default=False Whether to suppress warnings on generating conformers, e.g. UFFTYPER warnings. errors : {"raise", "ignore", "filter"}, default="raise" @@ -152,7 +152,7 @@ class ConformerGenerator(BasePreprocessor): "n_jobs": [Integral, None], "batch_size": [Integral, None], "verbose": ["verbose", dict], - "random_state": [Interval(Integral, left=-1, right=None, closed="left")], + "random_state": [Interval(Integral, left=-1, right=None, closed="left"), None], } def __init__( @@ -203,10 +203,10 @@ def transform_x_y( """ Generate conformers for molecules. - If ``errors`` is set to ``"filter"``, then in case of errors less than - n_samples values may be returned. If ``errors`` is set to ``"ignore"``, - some molecules may be returned without conformers generated and with - ``conf_id`` property set to -1. + If ``errors`` is set to ``"filter"``, then in case of errors fewer than + n_samples values may be returned (``n_samples_conf_gen <= n_samples``). + If ``errors`` is set to ``"ignore"``, some molecules may be returned without + conformers generated and with ``conf_id`` property set to -1. Parameters ---------- diff --git a/skfp/preprocessing/input_output/aminoseq.py b/skfp/preprocessing/input_output/aminoseq.py index 09a88bad..f157ca6c 100644 --- a/skfp/preprocessing/input_output/aminoseq.py +++ b/skfp/preprocessing/input_output/aminoseq.py @@ -62,14 +62,13 @@ class MolFromAminoseqTransformer(BasePreprocessor): Examples -------- >>> from skfp.preprocessing import MolFromAminoseqTransformer - >>> sequences = ["KWLRRVWRWWR","FLPAIGRVLSGIL","ILGKLLSTAWGLLSKL",] + >>> sequences = ["KWLRRVWRWWR","FLPAIGRVLSGIL","ILGKLLSTAWGLLSKL"] >>> mol_from_aminoseq = MolFromAminoseqTransformer() >>> mol_from_aminoseq MolFromAminoseqTransformer() >>> mol_from_aminoseq.transform(sequences) # doctest: +SKIP [, - , , ] """ @@ -118,7 +117,7 @@ def transform(self, X, copy: bool = False) -> list[Mol]: Returns ------- - X : list of shape (n_samples_conf_gen,) + X : list of shape (n_samples,) List with RDKit ``Mol`` objects. """ X = super().transform(X, copy) diff --git a/skfp/preprocessing/input_output/inchi.py b/skfp/preprocessing/input_output/inchi.py index d821d097..aefd2f71 100644 --- a/skfp/preprocessing/input_output/inchi.py +++ b/skfp/preprocessing/input_output/inchi.py @@ -49,7 +49,7 @@ class MolFromInchiTransformer(BasePreprocessor): verbose : int or dict, default=0 Controls the verbosity when processing molecules. If a dictionary is passed, it is treated as kwargs for ``tqdm()``, - and can be used to control the progress bar.. + and can be used to control the progress bar. References ---------- @@ -100,7 +100,7 @@ def __init__( def transform(self, X, copy: bool = False) -> list[Mol]: """ Create RDKit ``Mol`` objects from InChI strings. If ``valid_only`` is set to - True, returns only a subset of molecules which could be successfullyloaded. + True, returns only a subset of molecules which could be successfully loaded. Parameters ---------- @@ -112,7 +112,7 @@ def transform(self, X, copy: bool = False) -> list[Mol]: Returns ------- - X : list of shape (n_samples_conf_gen,) + X : list of shape (n_samples,) List with RDKit ``Mol`` objects. """ X = super().transform(X, copy) diff --git a/skfp/preprocessing/input_output/sdf.py b/skfp/preprocessing/input_output/sdf.py index 302d578c..39f6e5a0 100644 --- a/skfp/preprocessing/input_output/sdf.py +++ b/skfp/preprocessing/input_output/sdf.py @@ -61,7 +61,7 @@ def __init__( self.sanitize = sanitize self.remove_hydrogens = remove_hydrogens - def transform(self, X: str, copy: bool = False) -> list[Mol]: # type: ignore[override] # noqa: ARG002 + def transform(self, X: str, copy: bool = False) -> list[Mol]: # type: ignore[override] # noqa: ARG002 """ Create RDKit ``Mol`` objects from SDF file. @@ -75,7 +75,7 @@ def transform(self, X: str, copy: bool = False) -> list[Mol]: # type: ignore[ov Returns ------- - X : list of shape (n_samples_conf_gen,) + X : list of shape (n_samples,) List with RDKit ``Mol`` objects. """ self._validate_params() @@ -115,7 +115,7 @@ class MolToSDFTransformer(BasePreprocessor): Parameters ---------- filepath : string, default="mols.sdf" - A string with file path location to save the SDF file. Tt should be a valid + A string with file path location to save the SDF file. It should be a valid file path with ``.sdf`` extension. kekulize : bool, default=True diff --git a/skfp/preprocessing/input_output/smiles.py b/skfp/preprocessing/input_output/smiles.py index aa346698..646d82cc 100644 --- a/skfp/preprocessing/input_output/smiles.py +++ b/skfp/preprocessing/input_output/smiles.py @@ -115,7 +115,7 @@ def transform(self, X, copy: bool = False) -> list[Mol]: Returns ------- - X : list of shape (n_samples_conf_gen,) + X : list of shape (n_samples,) List with RDKit ``Mol`` objects. """ y = np.empty(len(X)) diff --git a/skfp/preprocessing/standardization.py b/skfp/preprocessing/standardization.py index bd45226e..ab420c50 100644 --- a/skfp/preprocessing/standardization.py +++ b/skfp/preprocessing/standardization.py @@ -116,7 +116,7 @@ def transform(self, X: Sequence[str | Mol], copy: bool = True) -> list[Mol]: Returns ------- - X : list of shape (n_samples_conf_gen,) + X : list of shape (n_samples,) List with RDKit ``Mol`` objects. """ return super().transform(X, copy) diff --git a/skfp/utils/parallel.py b/skfp/utils/parallel.py index 13a21511..5788d4c7 100644 --- a/skfp/utils/parallel.py +++ b/skfp/utils/parallel.py @@ -12,7 +12,7 @@ class ProgressParallel(Parallel): Parameters ---------- - tqdm_settings: Optional[dict] = None + tqdm_settings: dict or None, default=None Settings to use for the ``tqdm()`` progress bar. """ @@ -91,7 +91,7 @@ def run_in_parallel( Returns ------- X : list of length (n_samples,) - The processed data. If processing function returns functions, this will be + The processed data. If the processing function returns lists, this will be a list of lists. Examples diff --git a/skfp/utils/validators.py b/skfp/utils/validators.py index ff58650c..af53444c 100644 --- a/skfp/utils/validators.py +++ b/skfp/utils/validators.py @@ -10,7 +10,7 @@ def ensure_mols(X: Sequence[Any]) -> list[Mol]: """ Ensure that all input sequence elements are RDKit ``Mol`` objects. Requires all input elements to be of the same type: string (SMILES strings) or ``Mol``. - In case of SMILES strings, they are converted to RDKit ``Mol`` objects with + In the case of SMILES strings, they are converted to RDKit ``Mol`` objects with default settings. """ if not all(isinstance(x, (Mol, PropertyMol, str)) for x in X): @@ -31,7 +31,7 @@ def ensure_mols(X: Sequence[Any]) -> list[Mol]: def ensure_smiles(X: Sequence[Any]) -> list[str]: """ Ensure that all input sequence elements are SMILES strings. Requires all input - elements to be of the same type: string (SMILES strings) or ``Mol``. In case of + elements to be of the same type: string (SMILES strings) or ``Mol``. In the case of RDKit ``Mol`` objects, they are converted to SMILES strings with default settings. """ if not all(isinstance(x, (Mol, PropertyMol, str)) for x in X): @@ -44,7 +44,7 @@ def ensure_smiles(X: Sequence[Any]) -> list[str]: def require_mols(X: Sequence[Any]) -> None: """ - Check that all inputs are RDKit ``Mol`` objects, raises ValueError otherwise. + Check that all inputs are RDKit ``Mol`` objects, raises TypeError otherwise. """ for idx, x in enumerate(X): if not isinstance(x, (Mol, PropertyMol)): @@ -56,7 +56,7 @@ def require_mols(X: Sequence[Any]) -> None: def require_mols_with_conf_ids(X: Sequence[Any]) -> Sequence[Mol]: """ Check that all inputs are RDKit ``Mol`` objects with ``"conf_id"`` property - set, i.e. with conformers computed and properly identified. Raises ValueError + set, i.e. with conformers computed and properly identified. Raises TypeError otherwise. """ if not all(isinstance(x, (Mol, PropertyMol)) and x.HasProp("conf_id") for x in X): @@ -70,7 +70,7 @@ def require_mols_with_conf_ids(X: Sequence[Any]) -> Sequence[Mol]: def require_strings(X: Sequence[Any]) -> None: """ - Check that all inputs are strings, raises ValueError otherwise. + Check that all inputs are strings, raises TypeError otherwise. """ for idx, x in enumerate(X): if not isinstance(x, str): @@ -81,10 +81,11 @@ def require_strings(X: Sequence[Any]) -> None: def require_atoms(min_atoms: int = 1, only_explicit=True) -> Callable: """ - Decorator for functions operating on single molecule. Ensures it is - nonempty (by default) or has at least the specified number of atoms, - raises ValueError otherwise. - """ # noqa: D401 + Ensure molecule is nonempty or has at least min_atoms atoms. + + Used as a decorator for functions operating on a single molecule. + Raises ValueError if conditions are not met. + """ def decorator(func: Callable) -> Callable: @functools.wraps(func) diff --git a/tests/applicability_domain/utils.py b/tests/applicability_domain/utils.py index 60358f48..f245dbbe 100644 --- a/tests/applicability_domain/utils.py +++ b/tests/applicability_domain/utils.py @@ -43,8 +43,8 @@ def get_data_outside_ad( ) if binarize: - thresholds = np.median(X_train, axis=0) - X_train = (X_train > thresholds).astype(int) + threshold = np.percentile(X_train, 25) + X_train = (X_train > threshold).astype(int) X_test = 1 - X_train[:n_test] else: X_test = X_train[:n_test] + 100.0 diff --git a/tests/descriptors/charge.py b/tests/descriptors/charge.py index 9a547b23..6b2a3120 100644 --- a/tests/descriptors/charge.py +++ b/tests/descriptors/charge.py @@ -39,6 +39,17 @@ def test_atomic_partial_charges_gasteiger(mols_list, gasteiger_allowed_mols): ] +def test_atomic_partial_charges_precomputed(mols_list): + # set charge +1 everywhere + for mol in mols_list: + for atom in mol.GetAtoms(): + atom.SetDoubleProp("charge", 1) + + for mol in mols_list: + charges = atomic_partial_charges(mol, partial_charge_model="precomputed") + assert all(c == 1 for c in charges) + + def test_atomic_partial_charges_ignore_error(): organometallics = [ "CCCC[Li]", diff --git a/uv.lock b/uv.lock index 2d09d163..adc1656c 100644 --- a/uv.lock +++ b/uv.lock @@ -30,17 +30,16 @@ wheels = [ [[package]] name = "anyio" -version = "4.11.0" +version = "4.12.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "exceptiongroup", marker = "python_full_version < '3.11'" }, { name = "idna" }, - { name = "sniffio" }, { name = "typing-extensions", marker = "python_full_version < '3.13'" 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