Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel–Ligand Docking
Heesung Shim, Hyojin Kim, Jonathan Allen, Heike Wulff
Abstract
set. We demonstrate the effectiveness of our proposed classifiers on multiple evaluation data sets including the standard PDBbind CASF-2016 benchmark data set and various compound libraries with structurally different protein targets including an ion channel data set extracted from Protein Data Bank (PDB) and an in-house KCa3.1 inhibitor data set. Our experiments show that excluding false positive docking poses using the proposed classifiers improves virtual high-throughput screening to identify novel molecules against each target protein compared to the initial screen based on the docking scores.
Topics & Concepts
Virtual screeningDocking (animal)Computer scienceConvolutional neural networkDrug discoveryArtificial intelligenceChemical databaseProtein–ligand dockingMachine learningAutoDockProtein Data BankData miningProtein structureBioinformaticsChemistryBiologyIn silicoBiochemistryGeneNursingMedicineComputational Drug Discovery MethodsMachine Learning in Materials Science