Update README.md

README.md

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     - name: test
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+---
+
+# Aqueous Solubility Database (AqSolDB)
+
+AqSolDB is created by the Autonomous Energy Materials Discovery [AMD] research group, consists of aqueous solubility values of 
+9,982 unique compounds curated from 9 different publicly available aqueous solubility datasets. This openly accessible dataset, 
+which is the largest of its kind, and will not only serve as a useful reference source of measured solubility data, but also 
+as a much improved and generalizable training data source for building data-driven models.
+
+## 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 `B3DB` datasets, e.g.,
+
+    >>> B3DB_classification = datasets.load_dataset("maomlab/B3DB", name = "B3DB_classification")
+    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
+
+    >>> B3DB_classification
+    B3DB_classification
+    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 catboost model
+
+    import json
+    from datasets import load_set
+    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/B3DB', name = 'B3DB_classification')
+    
+    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-"])
+
+
+## About the DB3B
+
+### 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).
+
+
+### 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
+
+```
+