Cosmetic improvements

README.md

@@ -15,7 +15,7 @@ Argenomic is an open-source implementation of an illumination algorithm for opti
 
 ## Getting Started
 
-After installing the software and running the tests, a basic usage example of argenomic (i.e. the rediscovery of Celecoxib) can be called upon in the following manner:
+After installing the software and running the tests, a basic usage example of argenomic (i.e. the rediscovery of Troglitazone) can be called upon in the following manner:
 ```
 python3 illuminate.py generations=100
 ```

argenomic/infrastructure.py

@@ -68,7 +68,7 @@ def sample(self, size: int) -> List[Chem.Mol]:
         molecules, weights = map(list, zip(*pairs))
         return random.choices(molecules, k=size, weights=weights)
 
-    def sample_pairs(self, size: int, generation: float) -> List[Tuple[Chem.Mol, Chem.Mol]]:
+    def sample_pairs(self, size: int) -> List[Tuple[Chem.Mol, Chem.Mol]]:
         """
         Returns a list of pairs of elite molecules of the requisted length. 
         The elite molecules are randomly drawn, weighted by their fitness. 

configuration/config.yaml

@@ -1,5 +1,5 @@
 ---
-data_file: data/smiles/guacamol_initial_rediscovery_celecoxib.smi
+data_file: data/smiles/guacamol_intitial_rediscovery_troglitazone.smi
 batch_size: 40
 initial_size: 100
 workers: 1
@@ -25,7 +25,7 @@ descriptor:
   - - 40
     - 130
 fitness:
-  target: O=S(=O)(c3ccc(n1nc(cc1c2ccc(cc2)C)C(F)(F)F)cc3)N
+  target: "O=C1NC(=O)SC1Cc4ccc(OCC3(Oc2c(c(c(O)c(c2CC3)C)C)C)C)cc4"
   type: ECFP4
 arbiter:
   rules:

illuminate.py

@@ -34,7 +34,7 @@ def __init__(self, config) -> None:
     def __call__(self) -> None:
         self.initial_population()
         for generation in range(1, self.generations):
-            molecules = self.generate_molecules(generation)
+            molecules = self.generate_molecules()
             molecules = self.process_molecules(molecules)
             self.archive.add_to_archive(molecules)
             self.archive.store_data(generation)
@@ -55,10 +55,10 @@ def load_from_database(self) -> List[Molecule]:
         molecules = [Molecule(Chem.CanonSmiles(smiles), pedigree) for smiles in smiles_list]
         return molecules
 
-    def generate_molecules(self, generation) -> List[Molecule]:
+    def generate_molecules(self) -> List[Molecule]:
         molecules = []
         molecule_samples = self.archive.sample(self.batch_size)
-        molecule_sample_pairs = self.archive.sample_pairs(self.batch_size, generation)
+        molecule_sample_pairs = self.archive.sample_pairs(self.batch_size)
         for molecule in molecule_samples:
             molecules.extend(self.mutator(molecule)) 
         for molecule_pair in molecule_sample_pairs: