Evaluating the predictivity of virtual screening for ABL kinase inhibitors to hinder drug resistance

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Virtual screening methods are now widely used in early stages of drug discovery, aiming to rank potential inhibitors. However, any practical ligand set (of active or inactive compounds) chosen for deriving new virtual screening approaches cannot fully represent all relevant chemical space for potential new compounds. In this study, we have taken a retrospective approach to evaluate virtual screening methods for the leukemia target kinase ABL1 and its drug-resistant mutant ABL1-T315I. 'Dual active' inhibitors against both targets were grouped together with inactive ligands chosen from different decoy sets and tested with virtual screening approaches with and without explicit use of target structures (docking). We show how various scoring functions and choice of inactive ligand sets influence overall and early enrichment of the libraries. Although ligand-based methods, for example principal component analyses of chemical properties, can distinguish some decoy sets from active compounds, the addition of target structural information via docking improves enrichment, and explicit consideration of multiple target conformations (i.e. types I and II) achieves best enrichment of active versus inactive ligands, even without assuming knowledge of the binding mode. We believe that this study can be extended to other therapeutically important kinases in prospective virtual screening studies.

Original languageEnglish
JournalChemical Biology & Drug Design
Issue number5
Pages (from-to)506-19
Number of pages14
Publication statusPublished - Nov 2013

    Research areas

  • Algorithms, Area Under Curve, Binding Sites, Drug Evaluation, Preclinical, Drug Resistance, Neoplasm, Enzyme Activation, Humans, Ligands, Molecular Docking Simulation, Mutation, Principal Component Analysis, Protein Binding, Protein Kinase Inhibitors, Protein Structure, Tertiary, Proto-Oncogene Proteins c-abl, ROC Curve, Recombinant Proteins, Journal Article

ID: 172765641