AI Is a Viable Alternative to High Throughput Screening: A 318-Target Study

Authors

The Atomwise AIMS Program

Publication Date

4-2-2024

Journal

Scientific Reports

Abstract

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Keywords

High-Throughput Screening Assays, Small Molecule Libraries, Drug discovery, High-throughput screening, Virtual screening, Machine learning

Comments

This article has been corrected. See Sci Rep. 2024 Sep 16;14:21579.

Associated Data

PMID: 38565852