Evidence map›Paper›PMID 39237523›Full record

ArticleNature communications2024

An artificial intelligence accelerated virtual screening platform for drug discovery.

Guangfeng Zhou, Domnita-Valeria Rusnac, Hahnbeom Park, Daniele Canzani, Hai Minh Nguyen, Lance Stewart, Matthew F Bush, Phuong Tran Nguyen, Heike Wulff, Vladimir Yarov-Yarovoy and 2 more

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
118citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

118 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Accurate RNA-Ligand Binding Site Prediction Based on a Multi-Channel Graph Neural Network.Interdisciplinary sciences, computational life sciences · 2026
    Article
  3. Review
  4. Review
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  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Artificial intelligence virtual bone organoids (AIVBOs).Journal of orthopaedic translation · 2026
    Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article

58 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Guangfeng Zhou *Department of Biochemistry, University of Washington, Seattle, WA, USA.ORCID 0000-0003-2728-1917
Domnita-Valeria Rusnac *Howard Hughes Medical Institute, Department of Pharmacology, University of Washington, Seattle, WA, USA.
Hahnbeom ParkBrain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea.ORCID 0000-0002-7129-1912
Daniele CanzaniDepartment of Chemistry, University of Washington, Seattle, WA, USA.
Hai Minh NguyenDepartment of Pharmacology, University of California Davis, Davis, CA, USA.
Lance StewartInstitute for Protein Design, University of Washington, Seattle, WA, USA.ORCID 0000-0003-4264-5125
Matthew F BushDepartment of Chemistry, University of Washington, Seattle, WA, USA.ORCID 0000-0003-3526-4973
Phuong Tran NguyenDepartment of Physiology and Membrane Biology, University of California Davis, Davis, CA, USA.
Heike WulffDepartment of Pharmacology, University of California Davis, Davis, CA, USA.ORCID 0000-0003-4437-5763
Vladimir Yarov-YarovoyDepartment of Physiology and Membrane Biology, University of California Davis, Davis, CA, USA.ORCID 0000-0002-2325-4834
Ning ZhengHoward Hughes Medical Institute, Department of Pharmacology, University of Washington, Seattle, WA, USA. nzheng@uw.edu.ORCID 0000-0002-1039-1581
Frank DiMaioDepartment of Biochemistry, University of Washington, Seattle, WA, USA. dimaio@u.washington.edu.ORCID 0000-0002-7524-8938

Funding

User Training and OutreachP30GM124169 · NIGMS · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI Gregory L Hura · 2017 to 2026
$28.6M
Protein structure determination from low-resolution experimental dataR01GM123089 · NIGMS · UNIVERSITY OF WASHINGTON · PI DIMAIO, FRANK P · 2017 to 2025
$2.6M
National Research Foundation of Korea (NRF) 2022R1C1C1007817National Science Foundation (NSF) 1807382National Science Foundation (NSF) 2203513NIGMS NIH HHS P30 GM124169NIGMS NIH HHS R01 GM123089United States Department of Defense | Defense Advanced Research Projects Agency (DARPA) HR001120S0052United States Department of Defense | Defense Threat Reduction Agency (DTRA) GRANT13030960U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) P30 GM124169-01
6 · The paper itself

Abstract

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel Na

Indexed as

Artificial IntelligenceDrug DiscoveryMolecular Docking SimulationCrystallography, X-RayDrug Evaluation, PreclinicalHumansLigandsNAV1.7 Voltage-Gated Sodium ChannelProtein BindingSmall Molecule LibrariesUbiquitin-Protein LigasesLigandsNAV1.7 Voltage-Gated Sodium ChannelSmall Molecule LibrariesUbiquitin-Protein Ligases

Identifiers

PMID39237523
PMCPMC11377542

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.