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AI is a viable alternative to high throughput screening: A 318-target study.
Sci. Rep. 14:7526 (2024)
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.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Drug Discovery; Similarity; Accuracy; Docking; Design; Assays; Qsar; Identification; Aggregation; Information
ISSN (print) / ISBN
2045-2322
e-ISSN
2045-2322
Journal
Scientific Reports
Quellenangaben
Volume: 14,
Issue: 1,
Article Number: 7526
Publisher
Nature Publishing Group
Publishing Place
London
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Structural Biology (STB)