README.md

---
license: mit
language:
- en
tags:
- chemistry
- medicinal chemistry
pretty_name: AggregatorAdvisor
size_categories:
- 10K<n<100K
dataset_summary:
  12645 compounds from 20 sources from the AggregatorAdvisor release-2022/06
  (https://advisor.docking.org/) that are experimentally determined to aggregate
  thereby potentially causing false-positive outcomes in high-throughput drug screening.
dataset_description: >-
  Many drug-like molecules phase-separate in aqueous solutions causing false-positives
  in in biochemical assays often used for drug screening. The Aggregator Advisor
  (https://advisor.docking.org/) is a web-tool hosted by the Shoichet Lab at UCSF
  and used to assess the risk of a molecules being an aggregetor, may aggregate in
  biochemical assays based on the chemical similarity to known aggregators, and
  physical properties. This dataset includes the known aggregator from the
  Aggregator Advisor 2022/06 release curated from 20 sources. Since
  aggregation is dependent on the hydrophobicity of the compound, the predicted
  logP is also computed for each compound.
citation: |-
  @article 
    {Irwin2015, title = {An Aggregation Advisor for Ligand Discovery},
    volume = {58}, ISSN = {1520-4804}, 
    url = {http://dx.doi.org/10.1021/acs.jmedchem.5b01105}, 
    DOI = {10.1021/acs.jmedchem.5b01105}, 
    number = {17}, 
    journal = {Journal of Medicinal Chemistry}, 
    publisher = {American Chemical Society (ACS)}, 
    author = {Irwin, John J. and Duan,  Da and Torosyan,  Hayarpi and Doak,  Allison K. and
    Ziebart,  Kristin T. and Sterling,  Teague and Tumanian,  Gurgen and Shoichet,  Brian K.}, 
    year = {2015}, 
    month = aug, 
    pages = {7076–7087} 
    }
config_names:
- AggregatorAdvisor
configs:
- config_name: AggregatorAdvisor
  data_files:
  - split: test
    path: AggregatorAdvisor/test.csv
  - split: train
    path: AggregatorAdvisor/train.csv
dataset_info:
- config_name: AggregatorAdvisor
  features:
  - name: SMILES
    dtype: string
  - name: substance_id
    dtype: string
  - name: aggref_index
    dtype: int64
  - name: logP
    dtype: float64
  - name: reference
    dtype: string
  splits:
  - name: train
    num_bytes: 404768
    num_examples: 10116
  - name: test
    num_bytes: 101288
    num_examples: 2529
---

# Aggregator Advisor
The [Aggregator Advisor](https://advisor.docking.org/) is a web-tool hosted
by the Shoichet Lab at UCSF and used to assess the risk of a molecules being
an aggregetor, may aggregate in biochemical assays based on the chemical
similarity to known aggregators, and physical properties. The most current
release (2022/06) contains 12645 known aggregator molecules from 20 sources. 

The train and test datasets uploaded to our Hugging Face repository have been sanitized and split from the original dataset
If you want to try these processes with the original dataset, please follow the instructions in the [Processing Script.py](https://huggingface.co/datasets/maomlab/AggregatorAdvisor/blob/main/Preprocessing%20Script.py) file located in the AggregatorAdvisor.
The [raw_data.csv](https://huggingface.co/datasets/maomlab/AggregatorAdvisor/blob/main/raw_data.csv) is the original dataset from the paper, 
and the files in [AggregatorAdvisor](https://huggingface.co/datasets/maomlab/AggregatorAdvisor/tree/main/AggregatorAdvisor) are the sanitized version files that we made.

## 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 `AggregatorAdvisor` datasets, e.g.,

    >>> AggregatorAdvisor = datasets.load_dataset("maomlab/AggregatorAdvisor", name = "AggregatorAdvisor")
    Downloading readme: 100%|██████████| 4.70k/4.70k [00:00<00:00, 277kB/s]
    Downloading data: 100%|██████████| 530k/530k [00:00<00:00, 303kB/s]
    Downloading data: 100%|██████████| 2.16M/2.16M [00:00<00:00, 12.1MB/s]
    Generating test split: 100%|██████████| 2529/2529 [00:00<00:00, 29924.07 examples/s]
    Generating train split: 100%|██████████| 10116/10116 [00:00<00:00, 95081.99 examples/s]

and inspecting the loaded dataset

    >>> AggregatorAdvisor
    DatasetDict({
    test: Dataset({
        features: ['new SMILES', 'substance_id', 'aggref_index', 'logP', 'reference'],
        num_rows: 2529
    })
    train: Dataset({
        features: ['new SMILES', 'substance_id', 'aggref_index', 'logP', 'reference'],
        num_rows: 10116
    })
})

### 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 and evaluate the catboost model
    
    split_dataset = load_dataset('maomlab/AggregatorAdvisor', name = 'AggregatorAdvisor')
    
    split_featurised_dataset = featurise_dataset(
      split_dataset,
      column = "new SMILES",
      representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))

    model = load_model_from_dict({
    "name": "cat_boost_regressor",
    "config": {
        "x_features": ['new SMILES::morgan', 'new SMILES::maccs_rdkit'],
        "y_features": ['logP']}})

    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"]['logP'],
        predictions=preds["cat_boost_regressor::logP"])    

### Data splits
Here we have used the `Realistic Split` method described in [(Martin et al., 2018)](https://doi.org/10.1021/acs.jcim.7b00166) to split the AggregatorAdvisor dataset.

## Citation
If you use this dataset please cite:

    An Aggregation Advisor for Ligand Discovery
    John J. Irwin, Da Duan, Hayarpi Torosyan, Allison K. Doak, Kristin T. Ziebart, Teague Sterling, Gurgen Tumanian, Brian K. Shoichet,
    J. Med. Chem. 2015, 58, 17, 7076–7087
    DOI: https://doi.org/10.1021/acs.jmedchem.5b01105