Merge branch 'dev'

CHANGELOG.md

@@ -1,14 +1,13 @@
 # Change Log
-From v3.4.5 to v3.4.6
+From v3.4.6 to v3.4.7
 
 ## Fixes
 
-None.
+- Prevent the QSPRpred scorer from crashing when an empty list of molecules is supplied. The scorer now returns an empty list of scores and outputs a warning.
 
 ## Changes
 
-- For the generator CLI, environment variables are now read from the generator meta file automatically. Now unused arguments are removed from the CLI.
-- Compatibility updates to make the package work with the latest QSPRpred scorers in version 3.0.0 and higher. Older scorers will still work if an older version is installed alongside DrugEx. Only the unit tests will fail since the models used there assume QSPPRpred v3.0.0 or later.
+- update depency rdkit-pypi to rdkit
 
 ## Removed Features
 

drugex/about.py

@@ -1,6 +1,6 @@
 import os
 
-VERSION = "3.4.6"
+VERSION = "3.4.7"
 
 if os.path.exists(os.path.join(os.path.dirname(__file__), '_version.py')):
     from ._version import version

drugex/training/scorers/qsprpred.py

@@ -14,6 +14,9 @@ def __init__(self, model, invalids_score=0.0, modifier=None, **kwargs):
         self.kwargs = kwargs
 
     def getScores(self, mols, frags=None):
+        if len(mols) == 0:
+            logger.warning("No molecules to score. Returning empty list...")
+            return []
         parsed_mols = []
         if not isinstance(mols[0], str):
             invalids = 0

drugex/training/tests.py

@@ -159,19 +159,25 @@ class TestScorer(TestCase):
 
     def test_getScores(self):
         scorer = getPredictor()
+        # test with invalid
+        mols = ["CCO", "XXXX"]
+        scores = scorer.getScores(mols)
+        self.assertEqual(len(scores), len(mols))
+        # test with empty
+        mols = []
+        scores = scorer.getScores(mols)
+        self.assertEqual(len(scores), len(mols))
+        # test with valid
         mols = ["CCO", "CC"]
         scores = scorer.getScores(mols)
         self.assertEqual(len(scores), len(mols))
         self.assertTrue(all([isinstance(score, float) and score > 0 for score in scores]))
-
+        # test directly with RDKit mols
         mols = [Chem.MolFromSmiles("CCO"), Chem.MolFromSmiles("CC")]
         scores = scorer.getScores(mols)
         self.assertEqual(len(scores), len(mols))
         self.assertTrue(all([isinstance(score, float) and score > 0 for score in scores]))
 
-        mols = ["CCO", "XXXX"]  # test with invalid
-        scores = scorer.getScores(mols)
-        self.assertEqual(len(scores), len(mols))
 
 class TrainingTestCase(TestCase):
 
@@ -394,7 +400,8 @@ def test_sequence_rnn(self):
         self.assertTrue(type(monitor.getModel()) == collections.OrderedDict)
         self.assertTrue(monitor.allMethodsExecuted())
 
-        pretrained.generate(num_samples=10, evaluator=environment, drop_invalid=False)
+        pretrained.generate(num_samples=10, evaluator=environment, drop_invalid=False, raw_scores=True)
+        pretrained.generate(num_samples=10, evaluator=environment, drop_invalid=False, raw_scores=False)
 
     def test_graph_transformer(self):
         """
@@ -457,7 +464,13 @@ def test_graph_transformer(self):
         pretrained.generate([
             "c1ccncc1CCC",
             "CCO"
-        ], num_samples=1, evaluator=environment, drop_invalid=False)
+        ], num_samples=2, evaluator=environment, drop_invalid=False, raw_scores=True)
+        pretrained.generate([
+            "c1ccncc1CCC",
+            "CCO"
+        ], num_samples=2, evaluator=environment, drop_invalid=False, raw_scores=False)
+        pretrained.generate(input_dataset=pr_data_set_test, num_samples=20, evaluator=environment, drop_invalid=False, raw_scores=True)
+        pretrained.generate(input_dataset=pr_data_set_test, num_samples=20, evaluator=environment, drop_invalid=False, raw_scores=False)
 
     def test_graph_transformer_scaffold(self):
         """
@@ -501,6 +514,10 @@ def test_graph_transformer_scaffold(self):
         self.assertTrue(type(monitor.getModel()) == collections.OrderedDict)
         self.assertTrue(monitor.allMethodsExecuted())
 
+        # generate molecules
+        pretrained.generate(input_dataset=data_set, num_samples=5, evaluator=environment, drop_invalid=False, raw_scores=True)
+        pretrained.generate(frags, num_samples=5, evaluator=environment, drop_invalid=False, raw_scores=False)
+
     def test_sequence_transformer(self):
         """
         Test fragment-based sequence transformer model.

pyproject.toml

@@ -37,7 +37,7 @@ dependencies = [
   "torch >= 1.7.0",
   "matplotlib >= 2.0",
   "tqdm",
-  "rdkit-pypi",
+  "rdkit",
   "joblib",
   "gitpython",
   "networkx",