Update README.md

README.md

@@ -125,12 +125,12 @@ and inspecting the loaded dataset
     HLM
     DatasetDict({
       test: Dataset({
-         features: ['NO.', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'BBB+/BBB-', 'Inchi', 'threshold', 'reference', 'group', 'comments', 'ClusterNo', 'MolCount'],
-         num_rows: 1951
+         features: ['ChEMBL ID (source)', 'IUPAC Names', 'Smiles', 'Class', 'Dataset'],
+         num_rows: 1131
       })
       train: Dataset({
-          features: ['NO.', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'BBB+/BBB-', 'Inchi', 'threshold', 'reference', 'group', 'comments', 'ClusterNo', 'MolCount'],
-          num_rows: 5856
+          features: ['ChEMBL ID (source)', 'IUPAC Names', 'Smiles', 'Class', 'Dataset'],,
+          num_rows: 4771
       })
     })
 
@@ -158,18 +158,19 @@ then load, featurize, split, fit, and evaluate the a catboost model
       representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
 
     model = load_model_from_dict({
-        "name": "cat_boost_classifier",
+        "name": "cat_boost__regressor",
         "config": {
-            "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
-            "y_features": ['BBB+/BBB-']}})
-    
+            "x_features": ['smiles::morgan', 'smiles::maccs_rdkit'],
+            "y_features": ['log_solubility'],
+        })
+        
     model.train(split_featurised_dataset["train"])
     preds = model.predict(split_featurised_dataset["test"])
     
-    classification_suite = load_suite("classification")
+    regression_suite = load_suite("regression")
     
-    scores = classification_suite.compute(
-        references=split_featurised_dataset["test"]['BBB+/BBB-'],
-        predictions=preds["cat_boost_classifier::BBB+/BBB-"])
-
+    scores = regression_suite.compute(
+        references=split_featurised_dataset["test"]['Solubility'],
+        predictions=preds["cat_boost_regressor::Solubility"])        
 
+## Citation