Evidence map›Paper›PMID 32607441›Full record

ArticleACS central science2020

Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery.

Francesco Gentile, Vibudh Agrawal, Michael Hsing, Anh-Tien Ton, Fuqiang Ban, Ulf Norinder, Martin E Gleave, Artem Cherkasov

Abstract read
In one paragraph

Article in ACS central science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 175 papers.

0numbers the graph read from it
0cells of the map it votes in
175citing papers in PubMed
–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

175 citing papers in PubMed.

  1. Article
  2. Article
  3. Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. AI-driven drug design: a comprehensive review.Journal of computer-aided molecular design · 2026
    Review
  9. Review
  10. Article
  11. Circumventing the Synthesizability Problem in Generative Molecular Design.Journal of chemical information and modeling · 2026
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article

115 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

8 authors.

Francesco GentileVancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H3Z6, Canada.
Vibudh AgrawalVancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H3Z6, Canada.
Michael HsingVancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H3Z6, Canada.
Anh-Tien TonVancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H3Z6, Canada.
Fuqiang BanVancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H3Z6, Canada.
Ulf NorinderSwetox, Unit of Toxicology Sciences, Karolinska Institutet, Forskargatan 20, SE-151 36 Södertalje, Sweden.
Martin E GleaveVancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H3Z6, Canada.
Artem CherkasovVancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H3Z6, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drug discovery is a rigorous process that requires billion dollars of investments and decades of research to bring a molecule "from bench to a bedside". While virtual docking can significantly accelerate the process of drug discovery, it ultimately lags the current rate of expansion of chemical databases that already exceed billions of molecular records. This recent surge of small molecules availability presents great drug discovery opportunities, but also demands much faster screening protocols. In order to address this challenge, we herein introduce Deep Docking (

Identifiers

PMID32607441
PMCPMC7318080

What OpenQuestion holds

Textmetadata
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.