Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

Alessandro Lusci, Michael Browning, David Fooshee, Joshua Swamidass, Pierre Baldi

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Background: A number of algorithms have been proposed to predict the biological targets of diverse molecules. Some are structure-based, but the most common are ligand-based and use chemical fingerprints and the notion of chemical similarity. These methods tend to be computationally faster than others, making them particularly attractive tools as the amount of available data grows. Results: Using a ChEMBL-derived database covering 490,760 molecule-protein interactions and 3236 protein targets, we conduct a large-scale assessment of the performance of several target-prediction algorithms at predicting drug-target activity. We assess algorithm performance using three validation procedures: standard tenfold cross-validation, tenfold cross-validation in a simulated screen that includes random inactive molecules, and validation on an external test set composed of molecules not present in our database. Conclusions: We present two improvements over current practice. First, using a modified version of the influence-relevance voter (IRV), we show that using molecule potency data can improve target prediction. Second, we demonstrate that random inactive molecules added during training can boost the accuracy of several algorithms in realistic target-prediction experiments. Our potency-sensitive version of the IRV (PS-IRV) obtains the best results on large test sets in most of the experiments. Models and software are publicly accessible through the chemoinformatics portal at

Original languageEnglish
Article number63
JournalJournal of Cheminformatics
Issue number1
StatePublished - Dec 29 2015


  • Fingerprints
  • Influence-relevance voter
  • Large-scale
  • Molecular potency
  • Random inactive molecules
  • Target-prediction


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