Updates the docs of drd3, rasp, and foldx

Author: miguelgondu

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

@@ -5,7 +5,7 @@
 
 [*Rapid Stability Predictions* (RaSP)](https://github.com/KULL-Centre/_2022_ML-ddG-Blaabjerg) {cite:p}`Blaabjerg:RaSP:2023` predicts the stability of a protein using a supervised learning approach. Starting from features learned using self supervision {cite:p}`Boomsma:spherical_conv:2017`, RaSP learns to predict Rosetta scores using neural networks. The drawback being that only additive mutations could be computed simultaneously. We limit the edit distance to 1 (i.e. we only consider mutations that are one-away from the wildtype).
 
-This objective function is quite similar to [`foldx_stability`](./foldx_stability.md), and can be considered a drop-in replacement for single mutations. Be aware that the scales are different, though. 
+This objective function is quite similar to [`foldx_stability`](./foldx_stability.md), and can be considered a drop-in replacement for single mutations. Be aware that the scales are different. 
 
 ## Prerequisites
 

docs/protein-optimization/using_poli/objective_repository/drd3_docking.md

@@ -90,10 +90,6 @@ Running this objective function will create an `./oracle` folder on your working
 After the setup described above, you can simply run the following code from 
 
 ```python
-from pathlib import Path
-
-import numpy as np
-
 from poli import objective_factory
 
 # How to create
@@ -112,10 +108,3 @@ print(y0)  # [[-4.1]]
 :::
 
 ::::
-
-<!-- ## References
-
-:::{bibliography}
-:style: alpha
-
-::: -->

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

@@ -9,15 +9,15 @@ This objective function returns the solvent accesible surface area (SASA) of a w
 
 :::{note}
 
-If you are interested in computing both a protein's stability and it's SASA score, try `foldx_stability_and_sasa` instead! Just change the `name` in the `create` method below.
+If you are interested in computing **both** a protein's stability and it's SASA score, try `foldx_stability_and_sasa` instead! Just change the `name` in the `create` method below.
 
 :::
 
 ## Prerequisites
 
 - Have `foldx` installed, and available in your home directory. We expect the following files to be there:
   - `~/foldx/foldx`: the binary. You might need to rename it.
-  - `~/foldx/rotabase.txt`: a text file necessary for `foldx` to run.
+  - `~/foldx/rotabase.txt`: a text file necessary for `foldx` to run (if you are using `v4` of `foldx`).
 - A `wildtype_pdb_file`: a (repaired) pdb file of the wildtype.
 
 :::{admonition} We can repair the file for you
@@ -46,7 +46,7 @@ You can either run this objective function in your current environment (assuming
 You will have to install the following two dependencies:
 
 ```bash
-pip install biopython python-levenshtein
+pip install biopython python-levenshtein pdb-tools
 ```
 
 Then run

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

@@ -16,7 +16,7 @@ If you are interested in computing both a protein's stability and it's SASA scor
 
 - Have `foldx` installed, and available in your home directory. We expect the following files to be there:
   - `~/foldx/foldx`: the binary. You might need to rename it.
-  - `~/foldx/rotabase.txt`: a text file necessary for `foldx` to run.
+  - `~/foldx/rotabase.txt`: a text file necessary for `foldx` to run (only if you are using v4 of `foldx`).
 - A `wildtype_pdb_file`: a (repaired) pdb file of the wildtype.
 
 :::{admonition} We can repair the file for you
@@ -45,7 +45,7 @@ You can either run this objective function in your current environment (assuming
 You will have to install the following two dependencies:
 
 ```bash
-pip install biopython python-levenshtein
+pip install biopython python-levenshtein pdb-tools
 ```
 
 Then run

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

@@ -57,7 +57,7 @@ import numpy as np
 from poli import objective_factory
 
 # Using create
-_, f, x0, y0, _ = objective_factory.create(name="penalized_logp_lambo")
+problem_info, f, x0, y0, _ = objective_factory.create(name="penalized_logp_lambo")
 
 # An example input
 print(x0)