Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking
Dávid Bajusz | Anita Rácz | Károly Héberger
Ensemble docking is a widely applied concept in structure-based virtual screening&mdash:to at least partly account for protein flexibility&mdash: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&mdash: and in this study as well&mdash: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.
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