PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences-Reference-Cited by-同舟云学术

PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences

Published:2024 Issue: Volume: Page:
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Buttenschoen Martin¹^ORCID,Morris Garrett M.¹^ORCID,Deane Charlotte M.¹^ORCID

Affiliation:

1. Department of Statistics, 24-29 St Giles', Oxford OX1 3LB, UK

Abstract

PoseBusters assesses molecular poses using steric and energetic criteria. We find that classical protein-ligand docking tools currently still outperform deep learning-based methods.

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SC/D3SC04185A

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