fIxed / translated comments (#1)

eval/docking.py

@@ -29,24 +29,24 @@ def docking(
   receptor_name = os.path.splitext(receptor_file)[-1].split('.')[0]
   ligand_name = os.path.splitext(ligand_file)[-1].split('.')[0]
 
-  #On initialise vina
+  # initialises vina
   v = Vina(sf_name='vina', verbosity=1)
   v.set_receptor(receptor_file)
   v.set_ligand_from_file(ligand_file)
 
-  #On pose la box de docking
+  # set the docking frame
   v.compute_vina_maps(center=center,box_size=box_size)
 
-  # Score the current pose
+  # scores the current pose
   energy = v.score()
   print('Score before minimization: %.3f (kcal/mol)' % energy[0])
 
-  # Minimized locally the current pose
+  # minimizes locally the current pose
   energy_minimized = v.optimize()
   print('Score after minimization : %.3f (kcal/mol)' % energy_minimized[0])
   # v.write_pose(f'{ligand_name}_minimized.pdbqt', overwrite=True)
 
-  # Dock the ligand
+  # docks the ligand
   v.dock(exhaustiveness=n_dockings, n_poses=20)
 
   if write:

eval/prepare.py

@@ -8,14 +8,14 @@ def prepare(file_path:str) -> str:
     @return: path to the output PDBQT file
     """
 
-    # Define the output file name
+    # defines the output file name
     pdbqt_file = os.path.splitext(file_path)[0] + '.pdbqt'
     pdbqt_file = pdbqt_file.replace('raw', 'preped')
     
-    # Convert PDB to PDBQT using Open Babel
+    # converts PDB to PDBQT using Open Babel
     flags = "-xc -xr" if file_path.endswith("pdb") else ""
     command = f'obabel {file_path} -opdbqt -O {pdbqt_file} -h {flags}'
-    command += "--partialcharge gasteiger" # include forces and charges
+    command += "--partialcharge gasteiger" # includes forces and charges
     subprocess.run(command, shell=True)
 
     return pdbqt_file

eval/window.py

@@ -33,7 +33,7 @@ def compute_box(
     if site_atoms.size == 0:
         site_atoms = ligand_coords
 
-    # Compute min/max coordinates for the docking box
+    # compute min/max coordinates for the docking box
     x_min, y_min, z_min = np.min(site_atoms, axis=0)
     x_max, y_max, z_max = np.max(site_atoms, axis=0)
     

model/Model.py

@@ -34,7 +34,7 @@ class Model:
     if self.verbose == 2:
       print('Now initializing pytorch and CUDA environment :')
 
-    # clean cache and setting the libs seeds
+    # cleans cache and sets the libs seeds
     torch.cuda.empty_cache()
     seed_all(2089)
 
@@ -42,7 +42,7 @@ class Model:
       print('\tpytorch and CUDA initialized correctly.')
       print('Now retrieving alphabet from fair-ESM :')
 
-    # set ESM2 alphabet as the usual alphabet
+    # sets ESM2 alphabet as the usual alphabet
     pretrained_model, self.alphabet = esm.pretrained.load_model_and_alphabet_hub('esm2_t33_650M_UR50D')
     del pretrained_model # ESM2 pretrained_model that we don't need here is deleted from memory
 
@@ -50,7 +50,7 @@ class Model:
       print('\tESM alphabet successfully loaded.')
       print('Now building PocketGen model :')
 
-    # set the model and load the checkpoint from .pt file
+    # sets the model and load the checkpoint from .pt file
     self.checkpoint = torch.load(checkpoint_path, map_location=self.device)
 
     if self.verbose == 2:

model/featurize.py

@@ -10,8 +10,8 @@ def densify(data:dict) -> torch.Tensor:
   """
 
   return Compose([
-    FeaturizeProteinAtom(), # issue : star import
-    FeaturizeLigandAtom(), # issue : star import
+    FeaturizeProteinAtom(),
+    FeaturizeLigandAtom(),
   ])(data)
 
 
@@ -35,12 +35,12 @@ def featurize(
   @return (dict): a feature dictionnary
   """
 
-  # concatenate the first 3 dicts (prot, lig and residue)
+  # concatenates the first 3 dicts (prot, lig and residue)
   features = dict({f"p_{k}":v for k,v in protein_dict.items()}, 
     **{f"l_{k}":v for k,v in ligand_dict.items()})
   features.update(residue_dict)
 
-  # add keys for simple variables
+  # adds keys for simple variables
   features.update({
     'full_seq_index': full_seq_index,
     'r10_index': r10_index,

model/sampler.py

@@ -11,7 +11,7 @@ def interaction(ligand_path: str, receptor_path: str) -> torch.Tensor:
   @return (torch.Tensor): a data-dense feature tensor representing the interaction.
   """
 
-  # Read and parse the mol (pdb / sdf) files
+  # read and parses the mol (pdb / sdf) files
   pdb_block = open(receptor_path, 'r').read()
   protein = PDBProtein(pdb_block)
   ligand_dict = parse_sdf_file(ligand_path, feat=False)
@@ -20,12 +20,12 @@ def interaction(ligand_path: str, receptor_path: str) -> torch.Tensor:
   r10_index, r10_residues = protein.query_residues_ligand(ligand_dict, radius=10, selected_residue=None, return_mask=False)
   full_seq_index, full_seq_residues = protein.query_residues_ligand(ligand_dict, radius=3.5, selected_residue=r10_residues, return_mask=False)
 
-  # define pocket from the (r < 10) residues
+  # defines pocket from the (r < 10) residues
   pocket = PDBProtein(protein.residues_to_pdb_block(r10_residues))
   pocket_dict = pocket.to_dict_atom()
   residue_dict = pocket.to_dict_residue()
 
-  # define the scope of protein_edit_residue (sould be of type torch.Tensor[bool])
+  # defines the scope of protein_edit_residue (sould be of type torch.Tensor[bool])
   _, residue_dict['protein_edit_residue'] = pocket.query_residues_ligand(ligand_dict)
 
   full_seq_index.sort()
@@ -45,7 +45,7 @@ def interaction(ligand_path: str, receptor_path: str) -> torch.Tensor:
     r10_index=r10_index
   )
 
-  # Add metadata
+  # add metadata
   data.update({
     'protein_filename': receptor_path,
     'ligand_filename': ligand_path