metadata

license: mit
language:
  - en
tags:
  - chemistry
  - medicinal chemistry
pretty_name: AggregatorAdvisor
size_categories:
  - 10K<n<100K
dataset_summary: >-
  AggregatorAdvisor identifies molecules that are known to aggregate or may
  aggregate in biochemical assays. The approach is based on the chemical
  similarity to known aggregators, and physical properties.

  The train and test datasets uploaded to our Hugging Face repository have been
  sanitized and split from the original dataset, which contains 12645 compounds.
  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.
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: new 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

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"])

Citation

J. Med. Chem. 2015, 58, 17, 7076–7087 Publication Date:August 21, 2015 https://doi.org/10.1021/acs.jmedchem.5b01105