This paper introduces a new consensus scoring approach for merging the results of different virtual screening methods based on conditional probabilities. The technique is experimentally evaluated using several ligand-based virtual screening methods and compared to two variations of the established Sum-rank fusion method where it performs as well or better than the Sum-rank methods. Our experiments confirm that consensus scoring increases the number of active compounds retrieved with respect to the best individual methods on average.
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