title: Accurate and efficient target prediction using a potency-sensitive influence-relevance voter.
abstract: 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 http://chemdb.ics.uci.edu/.
authors.affiliation: School of Information and Computer Sciences, University of California, Irvine, Irvine, USA.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Lusci
authors.affiliation: Pathology and Immunology, Washington University in St. Louis, St. Louis, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Browning
authors.affiliation: School of Information and Computer Sciences, University of California, Irvine, Irvine, USA.
authors.forename: David
authors.initials: D
authors.lastname: Fooshee
authors.affiliation: Pathology and Immunology, Washington University in St. Louis, St. Louis, USA.
authors.forename: Joshua
authors.initials: J
authors.lastname: Swamidass
authors.affiliation: School of Information and Computer Sciences, University of California, Irvine, Irvine, USA.
authors.forename: Pierre
authors.initials: P
authors.lastname: Baldi
keywords: Fingerprints; Influence-relevance voter; Large-scale; Molecular potency; Random inactive molecules; Target-prediction