Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release

TL;DR

This paper presents a large-scale data release of over 4.2 billion molecules with pre-computed properties, supporting AI-driven drug discovery efforts against SARS-CoV-2.

Contribution

It provides the first extensive dataset combining molecular structures and properties to facilitate AI and HPC-based drug screening for COVID-19.

Findings

23 datasets with 4.2 billion molecules available

Pre-computed molecular fingerprints, images, and descriptors

Enables rapid AI model development for drug discovery

Abstract

Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One promising approach is to train machine learning (ML) and artificial intelligence (AI) tools to screen large numbers of small molecules. As a contribution to that effort, we are aggregating numerous small molecules from a variety of sources, using high-performance computing (HPC) to computer diverse properties of those molecules, using the computed properties to train ML/AI models, and then using the resulting models for screening. In this first data release, we make available 23 datasets collected from community sources representing over 4.2 B molecules enriched with pre-computed: 1) molecular fingerprints to aid similarity searches, 2) 2D images of molecules to enable exploration and application of image-based deep learning methods, and 3) 2D and 3D molecular descriptors to speed development of machine learning models. This data release encompasses structural information on the 4.2 B molecules and 60 TB of pre-computed data. Future releases will expand the data to include more detailed molecular simulations, computed models, and other products.