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

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

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1326-1335.e4
Issue number8
StatePublished - Aug 6 2019

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Molecular Biology


  • D3R
  • community resource
  • drug design data resource
  • drug docking
  • pose prediction


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