Comparison of data fusion methods as consensus scores for ensemble docking

Dávid Bajusz, Anita Rácz, Károly Héberger

Research output: Contribution to journalArticle

2 Citations (Scopus)


Ensemble docking is a widely applied concept in structure-based virtual screening—to at least partly account for protein flexibility—usually granting a significant performance gain at a modest cost of speed. From the individual, single-structure docking scores, a consensus score needs to be produced by data fusion: this is usually done by taking the best docking score from the available pool (in most cases—and in this study as well—this is the minimum score). Nonetheless, there are a number of other fusion rules that can be applied. We report here the results of a detailed statistical comparison of seven fusion rules for ensemble docking, on five case studies of current drug targets, based on four performance metrics. Sevenfold cross-validation and variance analysis (ANOVA) allowed us to highlight the best fusion rules. The results are presented in bubble plots, to unite the four performance metrics into a single, comprehensive image. Notably, we suggest the use of the geometric and harmonic means as better alternatives to the generally applied minimum fusion rule.

Original languageEnglish
Article number2690
Issue number15
Publication statusPublished - Jul 24 2019


  • AUC
  • Data fusion
  • Ensemble docking
  • ROC curve
  • SRD

ASJC Scopus subject areas

  • Analytical Chemistry
  • Chemistry (miscellaneous)
  • Molecular Medicine
  • Pharmaceutical Science
  • Drug Discovery
  • Physical and Theoretical Chemistry
  • Organic Chemistry

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