initial example usage using the MolFlux package

README.md

@@ -94,9 +94,11 @@ while the whole dataset has categorical (`BBB+` or `BBB-`) BBB permeability labe
 (7807 compounds). Some physicochemical properties of the molecules are also provided.
 
 
-## Usage
+## Quickstart Usage
+
+### Load a dataset in python
 Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
-First, from the commandline install the datasets package
+First, from the command line install the `datasets` library
 
     $ pip install datasets
 
@@ -128,17 +130,74 @@ and inspecting the loaded 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 a catboost model
+
+    import json
+    from molflux.datasets import load_dataset, featurise_dataset, split_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
+    
+    dataset = load_dataset('maomlab/B3DB', config_name = 'B3DB_classification')
+    
+    featurised_dataset = featurise_dataset(
+      dataset,
+      column = "SMILES",
+      representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
+    
+    shuffle_strategy = load_split_from_dict({
+        "name": "shuffle_split",
+        "presets": {
+            "train_fraction": 0.8,
+            "validation_fraction": 0.0,
+            "test_fraction": 0.2}})
+    split_featurised_dataset = next(split_dataset(featurised_dataset, shuffle_strategy))
+            
+    model = load_model_from_dict({
+        "name": "cat_boost_classifier",
+        "config": {
+            "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
+            "y_features": ['BBB+/BBB-']}})
+    
+    model.train(split_featurised_dataset["train"])
+    preds = model.predict(split_featurised_dataset["test"])
+    
+    classification_suite = load_suite("classification")
+    
+    scores = classification_suite.compute(
+        references=split_featurised_dataset["test"]['BBB+/BBB-'],
+        predictions=preds["cat_boost_classifier::BBB+/BBB-"])
+
+
+## About the DB3B
+
+### Features of *B3DB*
 
-## Data splits
-The original B3DB dataset does not define splits, so here we have used the `Realistic Split` method described
-in [(Martin et al., 2018)](https://doi.org/10.1021/acs.jcim.7b00166).
+1. The largest dataset with numerical and categorical values for Blood-Brain Barrier small molecules
+    (to the best of our knowledge, as of February 25, 2021).
 
+2. Inclusion of stereochemistry information with isomeric SMILES with chiral specifications if
+    available. Otherwise, canonical SMILES are used.
 
+3. Characterization of uncertainty of experimental measurements by grouping the collected molecular
+    data records.
 
+4. Extended datasets for numerical and categorical data with precomputed physicochemical properties
+    using [mordred](https://github.com/mordred-descriptor/mordred).
 
 
+### Data splits
+The original B3DB dataset does not define splits, so here we have used the `Realistic Split` method described
+in [(Martin et al., 2018)](https://doi.org/10.1021/acs.jcim.7b00166).
 
-## Citation
+### Citation
 Please use the following citation in any publication using our *B3DB* dataset:
 
 ```md
@@ -155,18 +214,5 @@ publisher = {Springer Nature}
 }
 ```
 
-## Features of *B3DB*
-
-1. The largest dataset with numerical and categorical values for Blood-Brain Barrier small molecules
-    (to the best of our knowledge, as of February 25, 2021).
-
-2. Inclusion of stereochemistry information with isomeric SMILES with chiral specifications if
-    available. Otherwise, canonical SMILES are used.
-
-3. Characterization of uncertainty of experimental measurements by grouping the collected molecular
-    data records.
-
-4. Extended datasets for numerical and categorical data with precomputed physicochemical properties
-    using [mordred](https://github.com/mordred-descriptor/mordred).