Update README.md

README.md

@@ -140,7 +140,7 @@ then, from within python load the datasets library
    
 and load one of the `Molecule3D` datasets, e.g.,
 
-    >>> Molecule3D = datasets.load_dataset('maomlab/Molecule3D', name = 'Molecule3D_random_split')
+    >>> Molecule3D = datasets.load_dataset('maomlab/Molecule3D', name = 'Molecule3D_random_split')   # can put 'Molecule3D_scaffold_split' for the name as well
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@@ -164,6 +164,50 @@ and inspecting the dataset
     })
 }) 
 
+
+### Use a dataset to train a model
+One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia.
+First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support
+
+    pip install 'molflux[catboost,rdkit]'
+
+then load, featurize, split, fit, and evaluate the catboost model
+
+    import json
+    from datasets import load_dataset
+    from molflux.datasets import featurise_dataset
+    from molflux.features import load_from_dicts as load_representations_from_dicts
+    from molflux.splits import load_from_dict as load_split_from_dict
+    from molflux.modelzoo import load_from_dict as load_model_from_dict
+    from molflux.metrics import load_suite
+    
+    split_dataset = load_dataset('maomlab/Molecule3D', name = 'Molecule3D_random_split')  # can put 'Molecule3D_scaffold_split' for the name as well
+    
+    split_featurised_dataset = featurise_dataset(
+      split_dataset,
+      column = "SMILES",
+      representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
+
+    model = load_model_from_dict({
+        "name": "cat_boost_regressor",
+        "config": {
+            "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
+            "y_features": ['Solubility']}})
+    
+    model.train(split_featurised_dataset["train"])
+    preds = model.predict(split_featurised_dataset["test"])
+    
+    regression_suite = load_suite("regression")
+    
+    scores = regression_suite.compute(
+        references=split_featurised_dataset["test"]['Solubility'],
+        predictions=preds["cat_boost_regressor::Solubility"])    
+
+
+    
+
+
+
 ## Citation
 @misc{https://doi.org/10.48550/arxiv.2110.01717,
   doi = {10.48550/ARXIV.2110.01717},