Update README.md

README.md

@@ -8,4 +8,80 @@ size_categories:
 - 1K<n<10K
 config_names:
 - HematoxLong2022
----
+---
+
+# Hematotoxicity Dataset (HematoxLong2022)
+Initially, a total of 2670 compounds were collected and all molecules were subjected to the multistep data preparation process to ensure data quality.
+Finally, a hematotoxicity dataset containing 1772 chemicals was obtained, which includes a positive set with 589 molecules and a negative set with 1183 molecules.
+All of the molecules were divided into a training set of 1330 molecules and a test set of 442 molecules according to their Murcko scaffolds. 
+Additionally, 610 new molecules from related research and databases were compiled as the external validation set.
+
+## 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 `HematoxLong2022` datasets, e.g.,
+
+    >>> HematoxLong2022 = datasets.load_dataset("maomlab/HematoxLong2022", name = "HematoxLong2022")
+
+
+and inspecting the loaded dataset
+
+    >>> HematoxLong2022
+    HematoxLong2022
+    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/HematoxLong2022', name = 'HematoxLong2022')
+    
+    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-"])