Bioinformatics · 2019 · 76 citations · 11 references
PharmacyBioinformaticsMedicinal ChemistryComputational ChemistryDrug DesignDrug DiscoveryComputational Drug DiscoveryLigand BindingMolecular DockingProtein-ligand BindingProtein-ligand DockingVirtual ScreeningBiasesBinding Mode PredictionDocking-based Drug DesignLigand-protein DockingPharmacophore-guided DockingProtein-ligand Complex ModelingTarget-ligand InteractionsVirtual Screening Validation
SUMMARY: The performance of docking calculations can be improved by tuning parameters for the system of interest, e.g. biasing the results towards the formation of relevant protein-ligand interactions, such as known ligand pharmacophore or interaction sites derived from cosolvent molecular dynamics. AutoDock Bias is a straightforward and easy to use script-based method that allows the introduction of different types of user-defined biases for fine-tuning AutoDock4 docking calculations. AVAILABILITY AND IMPLEMENTATION: AutoDock Bias is distributed with MGLTools (since version 1.5.7), and freely available on the web at http://ccsb.scripps.edu/mgltools/ or http://autodockbias.wordpress.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Computational protein–ligand docking and virtual drug screening with the AutoDock suite
Stefano Forli, Ruth Huey, Michael E. Pique et al. · Nature Protocols · 2016 · 2.7K citations · Full text