Mol opt

README.md

@@ -46,7 +46,7 @@ Instructions coming soon...
 ### Molecular Optimization 🎯
 Running the Optimization Algorithm requires two steps:
 
-**Step 1.** Define the Oracle, which is responsible to evaluate the oracle score for the given molecules. Below is presented the Oracle implementation scheme.
+**Step 1.** Define the Oracle, which is responsible to evaluate the oracle scores for the given molecules. Below is presented the Oracle implementation scheme.
 
 ```python
 class ExampleOracle:
@@ -97,7 +97,7 @@ rej_sample_config:
     ... fine tuning hyperparameters ...
 ```
 
-Calling the **optimize** function.
+Call the **optimize** function.
 
 ```python
 from chemlactica.mol_opt.optimization import optimize
@@ -119,7 +119,7 @@ optimize(
 )
 ```
 
-Refer to [example_run.py](https://github.com/YerevaNN/ChemLactica/blob/main/chemlactica/mol_opt/example_run.py) for a full working example of an optimization run. For more complex examples refer to the [ChemlacticaTestSuit]() repository [mol_opt]() and [retmol]() directories.
+Refer to [example_run.py](https://github.com/YerevaNN/ChemLactica/blob/main/chemlactica/mol_opt/example_run.py) for a full working example of an optimization run. For more complex examples refer to the [ChemlacticaTestSuit]() repository [mol_opt](https://github.com/YerevaNN/ChemLacticaTestSuite/tree/master/mol_opt) and [retmol](https://github.com/YerevaNN/ChemLacticaTestSuite/tree/master/retmol) directories.
 
 ## Tests
 The test for running the a small sized model with the same

chemlactica/mol_opt/chemlactica_125m_hparams.yaml

@@ -1,5 +1,5 @@
-checkpoint_path: /path/to/model_dir
-tokenizer_path: /path/to/tokenizer_dir
+checkpoint_path: yerevann/chemlactica-125m
+tokenizer_path: yerevann/chemlactica-125m
 pool_size: 10
 validation_perc: 0.2
 num_mols: 0

chemlactica/mol_opt/example_run.py

@@ -22,7 +22,7 @@ def __init__(self, max_oracle_calls: int):
         self.mol_buffer = {}
 
         # the maximum possible oracle score or an upper bound
-        self.max_possible_oracle_score = 1.0
+        self.max_possible_oracle_score = 800
 
         # if True the __call__ function takes list of MoleculeEntry objects
         # if False (or unspecified) the __call__ function takes list of SMILES strings
@@ -39,16 +39,14 @@ def __call__(self, molecules: List[MoleculeEntry]):
             else:
                 try:
                     tpsa = rdMolDescriptors.CalcTPSA(molecule.mol)
-                    tpsa_score = min(tpsa / 1000, 1)
+                    oracle_score = tpsa
                     weight = rdMolDescriptors.CalcExactMolWt(molecule.mol)
-                    if weight <= 349:
-                        weight_score = 1
-                    elif weight >= 500:
-                        weight_score = 0
-                    else:
-                        weight_score = -0.00662 * weight + 3.31125
-
-                    oracle_score = (tpsa_score + weight_score) / 3
+                    num_rings = rdMolDescriptors.CalcNumRings(molecule.mol)
+                    if weight >= 350:
+                        oracle_score = 0
+                    if num_rings < 2:
+                        oracle_score = 0
+
                 except Exception as e:
                     print(e)
                     oracle_score = 0
@@ -105,7 +103,7 @@ def parse_arguments():
     for i in range(args.n_runs):
         set_seed(seeds[i])
         oracle = TPSA_Weight_Oracle(max_oracle_calls=1000)
-        config["log_dir"] = os.path.join(args.output_dir, "results_tpsa+weight+num_rungs.log")
+        config["log_dir"] = os.path.join(args.output_dir, f"results_chemlactica_tpsa+weight+num_rungs_{seeds[i]}.log")
         config["max_possible_oracle_score"] = oracle.max_possible_oracle_score
         optimize(
             model, tokenizer,

chemlactica/mol_opt/optimization.py

@@ -36,13 +36,15 @@ def create_optimization_entries(num_entries, pool, config):
     return optim_entries
 
 
-def create_molecule_entry(output_text):
+def create_molecule_entry(output_text, validate_smiles):
     start_smiles_tag, end_smiles_tag = "[START_SMILES]", "[END_SMILES]"
     start_ind = output_text.rfind(start_smiles_tag)
     end_ind = output_text.rfind(end_smiles_tag)
     if start_ind == -1 or end_ind == -1:
         return None
     generated_smiles = output_text[start_ind+len(start_smiles_tag):end_ind]
+    if not validate_smiles(generated_smiles):
+        return None
     if len(generated_smiles) == 0:
         return None
 
@@ -58,7 +60,8 @@ def create_molecule_entry(output_text):
 def optimize(
         model, tokenizer,
         oracle, config,
-        additional_properties={}
+        additional_properties={},
+        validate_smiles=lambda x:True
     ):
     file = open(config["log_dir"], "w")
     print("config", config)