maomlab/TDC

Therapeutics Data Commons

Therapeutics Data Commons(TDC) provides a publicly available collection of 22 machine learning tasks for therapeutic discovery. Our Hugging Face repository is a mirror of single-instance prediction tasks of TDC, encompassing a total of 46,265,659 data points.

The parquet files uploaded to our Hugging Face repository have been sanitized and curated from the original datasets.

• Each parquet file corresponds to a separate category (e.g., ADME) and contains multiple tasks (e.g., solubility, permeability).
• Each file follows its own schema (i.e., different column names) and has been preprocessed differently depending on the category.
• As ADME, Tox, and HTS datasets contain SMILES strings, we have sanitized (standardized) the SMILES strings. The sanitization process includes removing salts, standardizing the SMILES strings to a canonical form, etc. 2 invalid SMILES strings from ADME dataset and 59 invalid SMILES strings from HTS dataset were removed during the sanitization process.

A summary file (i.e., single_instance_prediction_summary.csv) is also uploaded, which lists:

• Problem (Category),
• Task,
• DatasetName,
• NumCompounds,
• Leaderboard information (Unit, Task, Metric, Dataset Split - https://tdcommons.ai/benchmark/admet_group/overview/)
• License,
• DatasetDescription,
• TaskDescription,
• Reference

Quick 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

Now load one of the 'TDC' datasets, e.g.,

>>> dataset = datasets.load_dataset("maomlab/TDC", name = "ADME")

You can modify "name" based on your interest (e.g., "Tox").