Cleans up the remaining documentation of black boxes

Author: miguelgondu

docs/protein-optimization/using_poli/objective_repository/rdkit_logp.md

@@ -9,7 +9,7 @@ This objective function returns the partition coefficient of a solute between oc
 
 ## Prerequisites
 
-- You will need to specify an alphabet `List[str]`.
+None. This black box should work out-of-the-box.
 
 ## How to run
 
@@ -32,14 +32,10 @@ import numpy as np
 
 from poli import objective_factory
 
-# Your alphabet
-alphabet = ["", "[C]", ...]
-
 # How to create
 problem_info, f, x0, y0, run_info = objective_factory.create(
     name="rdkit_logp",
-    alphabet=alphabet,
-    string_representation="SELFIES",  # it is "SMILES" by default.
+    string_representation="SELFIES",  # Can be either SMILES or SELFIES
     force_register=True, 
 )
 
@@ -57,21 +53,14 @@ print(f(x))  # Should be close to 0.6361
 If you want us to handle dependencies, run
 
 ```python
-from pathlib import Path
-
 import numpy as np
 
 from poli import objective_factory
 
-
-# Your alphabet
-alphabet = ["", "[C]", ...]
-
 # How to create
 problem_info, f, x0, y0, run_info = objective_factory.create(
     name="rdkit_logp",
-    alphabet=alphabet,
-    string_representation="SELFIES",  # it is "SMILES" by default.
+    string_representation="SELFIES",  # Can be either SMILES or SELFIES
     force_register=True, 
 )
 

docs/protein-optimization/using_poli/objective_repository/rdkit_qed.md

@@ -9,7 +9,7 @@ This objective function returns the Quantitative Estimate of Druglikeness (QED)
 
 ## Prerequisites
 
-- You will need to specify an alphabet `List[str]`.
+None. This black box should work out-of-the-box.
 
 ## How to run
 
@@ -31,14 +31,10 @@ Then run
 import numpy as np
 from poli import objective_factory
 
-# Your alphabet
-alphabet = ["", "[C]", ...]
-
 # How to create
 problem_info, f, x0, y0, run_info = objective_factory.create(
     name="rdkit_qed",
-    alphabet=alphabet,
-    string_representation="SELFIES",  # it is "SMILES" by default.
+    string_representation="SELFIES",  # Can be either SMILES or SELFIES
     force_register=True,
 )
 
@@ -48,7 +44,6 @@ x = np.array(["[C]"]).reshape(1, -1)
 # Querying:
 y = f(x)
 print(y)  # Should be close to 0.35978494
-assert np.isclose(y, 0.35978494).all()
 ```
 
 :::
@@ -61,14 +56,11 @@ If you want us to handle dependencies, run
 import numpy as np
 from poli import objective_factory
 
-# Your alphabet
-alphabet = ["", "[C]", ...]
-
 # How to create
 problem_info, f, x0, y0, run_info = objective_factory.create(
     name="rdkit_qed",
     alphabet=alphabet,
-    string_representation="SELFIES",  # it is "SMILES" by default.
+    string_representation="SELFIES",  # Can be either SMILES or SELFIES
     force_register=True,
 )
 

docs/protein-optimization/using_poli/objective_repository/sa_tdc.md

@@ -21,8 +21,6 @@ This can easily be done by running
 conda env create --file src/poli/objective_repository/sa_tdc/environment.yml
 ```
 
-This particular example _doesn't_ need to have the `lambo` package installed.
-
 ## How to run
 
 You can only run this objective function either in the `poli__lambo` environment, or as an isolated process (which runs this environment underneath).

docs/protein-optimization/using_poli/objective_repository/toy_continuous_problems.md

@@ -62,7 +62,7 @@ print(f(x))  # Should be [[0.0]] in this example
 
 ## Creating problems with low intrinsic dimensionality
 
-Some optimization algorithms (like [LineBO]() or [SAASBO]()) rely on the assumption that there is a _low intrinsic dimensionality_ to the problem. Roughly speaking, this means that only a subset of the variables are actually relevant to the problem in question. This `poli` objective allows you to create such problems. For example, consider `camelback_2d` (which is usually only defined in two dimensions). You can embed this function into, say, 30 dimensions by creating the objective as follows:
+Some optimization algorithms (like [LineBO](https://arxiv.org/abs/1902.03229) or [SAASBO](https://proceedings.mlr.press/v161/eriksson21a.html)) rely on the assumption that there is a _low intrinsic dimensionality_ to the problem. Roughly speaking, this means that only a subset of the variables are actually relevant to the problem in question. This `poli` objective allows you to create such problems. For example, consider `camelback_2d` (which is usually only defined in two dimensions). You can embed this function into, say, 30 dimensions by creating the objective as follows:
 
 ```python
 problem_info, f, x0, y0, run_info = objective_factory.create(