Evidence map›Paper›PMID 31257108›Full record

ArticleStructure (London, England : 1993)2019

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking.

Jeffrey R Wagner, Christopher P Churas, Shuai Liu, Robert V Swift, Michael Chiu, Chenghua Shao, Victoria A Feher, Stephen K Burley, Michael K Gilson, Rommie E Amaro

Abstract read
In one paragraph

Article in Structure (London, England : 1993), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Evaluating generalization in protein-ligand cofolding methods.Nature structural & molecular biology · 2026
    Article
  2. Article
  3. Article
  4. Blind Challenges Let Us See the Path Forward for Predictive Models.Journal of chemical information and modeling · 2026
    Article
  5. Review
  6. Article
  7. Article
  8. CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13.Journal of chemical information and modeling · 2025
    Article
  9. The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks.Journal of chemical information and modeling · 2025
    Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article
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

10 authors.

Jeffrey R WagnerDrug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA.
Christopher P ChurasDrug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA.
Shuai LiuDrug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA.
Robert V SwiftDrug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA.
Michael ChiuDrug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA.
Chenghua ShaoRCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Victoria A FeherDrug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA.
Stephen K BurleyRCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Michael K GilsonDrug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA. Electronic address: mgilson@ucsd.edu.
Rommie E AmaroDrug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA; Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA. Electronic address: ramaro@ucsd.edu.

Funding

MOLECULAR BIOPHYSICS TRAINING PROGRAMT32GM008326 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KOMIVES, ELIZABETH A. · 1989 to 2020
$7.5M
AN OPEN RESOURCE TO ADVANCE COMPUTER-AIDED DRUG DESIGNU01GM111528 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI AMARO, ROMMIE E, GILSON, MICHAEL K. · 2014 to 2018
$3.6M
NIGMS NIH HHS T32 GM008326NIGMS NIH HHS U01 GM111528
6 · The paper itself

Abstract

Docking calculations can accelerate drug discovery by predicting the bound poses of ligands for a targeted protein. However, it is not clear which docking methods work best. Furthermore, predicting poses requires steps outside the docking algorithm itself, such as preparation of the protein and ligand, and it is not known which components are most in need of improvement. The Continuous Evaluation of Ligand Protein Predictions (CELPP) is a blinded prediction challenge designed to address these issues. Participants create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow's predictions and posts the scores online. The results can be used to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.

Indexed as

Binding SitesComputational BiologyCrystallography, X-RayDrug DesignLigandsMolecular Docking SimulationProtein BindingProtein ConformationProteinsStructure-Activity RelationshipLigandsProteinsCELPPcommunity resourceD3Rdrug design data resourcedrug dockingpose predictionRCSB PDB

Identifiers

PMID31257108
PMCPMC6685748

What OpenQuestion holds

Textmetadata
LicenceTDM
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.