A practical guide to machine-learning scoring for structure-based virtual screening-Reference-Cited by-同舟云学术

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A practical guide to machine-learning scoring for structure-based virtual screening

Published:2023-10-16 Issue:11 Volume:18 Page:3460-3511
ISSN:1754-2189
Container-title:Nature Protocols
language:en
Short-container-title:Nat Protoc

Author:

Tran-Nguyen Viet-Khoa^ORCID,Junaid Muhammad,Simeon Saw^ORCID,Ballester Pedro J.^ORCID

Funder

Royal Society

Wolfson Foundation

Indo-French Centre for the Promotion of Advanced Research

Agence Nationale de la Recherche

Fondation ARC pour la Recherche sur le Cancer

Publisher

Springer Science and Business Media LLC

Subject

General Biochemistry, Genetics and Molecular Biology

Link

https://www.nature.com/articles/s41596-023-00885-w.pdf

Reference166 articles.

1. Pereira, D. A. & Williams, J. A. Origin and evolution of high throughput screening. Br. J. Pharmacol. 152, 53–61 (2007).

2. Wang, Y., Cheng, T. & Bryant, S. H. PubChem BioAssay: a decade’s development toward open high-throughput screening data sharing. SLAS Discov. 22, 655–666 (2017).

3. Payne, D. J., Gwynn, M. N., Holmes, D. J. & Pompliano, D. L. Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat. Rev. Drug Discov. 6, 29–40 (2007).

4. Heifetz, A., Southey, M., Morao, I., Townsend-Nicholson, A. & Bodkin, M. J. Computational methods used in hit-to-lead and lead optimization stages of structure-based drug discovery. Methods Mol. Biol. 1705, 375–394 (2018).

5. Jorgensen, W. L. Efficient drug lead discovery and optimization. Acc. Chem. Res. 42, 724–733 (2009).

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