Datasets:
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 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 file located in the AggregatorAdvisor. The raw_data.csv is the original dataset from the paper, and the files in 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 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 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) 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