AI is a viable alternative to high throughput screening: a 318-target study
File(s)
Published version
Author(s)
Type
Journal Article
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.
Date Issued
2024-04-02
Date Acceptance
2024-02-15
Citation
Scientific Reports, 2024, 14
ISSN
2045-2322
Publisher
Nature Portfolio
Journal / Book Title
Scientific Reports
Volume
14
Copyright Statement
© The Author(s) 2024, corrected publication 2024 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/38565852
PII: 10.1038/s41598-024-54655-z
Subjects
ACCURACY
AGGREGATION
ASSAYS
DESIGN
DOCKING
DRUG DISCOVERY
IDENTIFICATION
INFORMATION
Multidisciplinary Sciences
QSAR
Science & Technology
Science & Technology - Other Topics
SIMILARITY
Publication Status
Published
Coverage Spatial
England
Article Number
7526
Date Publish Online
2024-04-02