Update README.md

README.md

@@ -87,6 +87,89 @@ dataset_info:
         num_examples: 596  
 ---
 
-Human & Rat Liver Microsomal Stability
---
+# Human & Rat Liver Microsomal Stability
+3345 RLM and 6420 HLM compounds were initially collected from the ChEMBL bioactivity database. 
+(HLM ID: 613373, 2367379, and 612558; RLM ID: 613694, 2367428, and 612558)
+Finally, the RLM stability data set contains 3108 compounds, and the HLM stability data set contains 5902 compounds.
+For the RLM stability data set, 1542 (49.6%) compounds were classified as stable, and 1566 (50.4%) compounds were classified as unstable, 
+among which the training and test sets contain 2512 and 596 compounds, respectively. 
+The experimental data from the National Center for Advancing Translational Sciences (PubChem AID 1508591) were used as the external set. 
+For the HLM data set, 3799 (64%) compounds were classified as stable, and 2103 (36%) compounds were classified as unstable. 
+In addition, an external set from Liu et al.12 was used to evaluate the predictive power of the HLM model.
+
+
+## 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 command line install the `datasets` library
+
+    $ pip install datasets
+
+then, from within python load the datasets library
+
+    >>> import datasets
+   
+and load one of the `HLM_RLM` datasets, e.g.,
+
+    >>> HLM = datasets.load_dataset("maomlab/HLM_RLM", name = "HLM")
+    Downloading readme: 100%|████████████████████████| 4.40k/4.40k [00:00<00:00, 1.35MB/s]
+    Downloading data: 100%|██████████████████████████| 680k/680k [00:00<00:00, 946kB/s]
+    Downloading data: 100%|██████████████████████████| 2.11M/2.11M [00:01<00:00, 1.28MB/s]
+    Generating test split: 100%|█████████████████████| 1951/1951 [00:00<00:00, 20854.95 examples/s]
+    Generating train split: 100%|████████████████████| 5856/5856 [00:00<00:00, 144260.80 examples/s]
+
+and inspecting the loaded dataset
+
+    >>> HLM
+    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
+      })
+      train: Dataset({
+          features: ['NO.', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'BBB+/BBB-', 'Inchi', 'threshold', 'reference', 'group', 'comments', 'ClusterNo', 'MolCount'],
+          num_rows: 5856
+      })
+    })
+
+### 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 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/HLM_RLM', name = 'HLM')
+    
+    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_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-"])
+