Journal of Chemical Information and Modeling · 2020 · 230 citations · 65 references
Natural SciencesBiophysicsChemistryComputational ChemistryDrug DesignStructural BiologyBinding Affinity PredictionComputational Drug DiscoveryForce FieldsLigand BindingMolecular ModelingProtein-ligand DockingBinding Affinity AssessmentBiomolecular Binding AffinityDrug - Target Binding Affinity PredictionDrug-target Binding Affinity PredictionProtein - Ligand Affinity PredictionProtein-ligand Binding Affinity PredictionProtein-ligand Binding Free Energy CalculationsStructure-based Binding Prediction
Free-energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free-energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace, and OpenMM and uses the AMBER 14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset, the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05, which are broadly comparable to or better than previously reported results using other methods.
65
Helen M. Berman · Nucleic Acids Research · 2000 · 39.8K citations
Development and testing of a general amber force field
Junmei Wang, Romain M. Wolf, James W. Caldwell et al. · Journal of Computational Chemistry · 2004 · 19.6K citations