Literature DB >> 20373803

ConfGen: a conformational search method for efficient generation of bioactive conformers.

K Shawn Watts1, Pranav Dalal, Robert B Murphy, Woody Sherman, Rich A Friesner, John C Shelley.   

Abstract

We describe the methodology, parametrization, and application of a conformational search method, called ConfGen, designed to efficiently generate bioactive conformers. We define efficiency as the ability to generate a bioactive conformation within a small total number of conformations using a reasonable amount of computer time. The method combines physics-based force field calculations with empirically derived heuristics designed to achieve efficient searching and prioritization of the ligand's conformational space. While many parameter settings are supported, four modes spanning a range of speed and quality trades-offs are defined and characterized. The validation set used to test the method is composed of ligands from 667 crystal structures covering a broad array of target and ligand classes. With the fastest mode, ConfGen uses an average of 0.5 s per ligand and generates only 14.3 conformers per ligand, at least one of which lies within 2.0 A root-mean-squared deviation of the crystal structure for 96% of the ligands. The most computationally intensive mode raises this recovery rate to 99%, while taking 8 s per ligand. Combining multiple search modes to "fill-in" holes in the conformation space or energy minimizing using an all-atom force field each lead to improvements in the recovery rates at higher resolutions. Overall, ConfGen is at least as good as competing programs at high resolution and demonstrates higher efficiency at resolutions sufficient for many downstream applications, such as pharmacophore modeling.

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Year:  2010        PMID: 20373803     DOI: 10.1021/ci100015j

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  77 in total

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5.  Binding Mode Prediction and Virtual Screening Applications by Covalent Docking.

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Journal:  Mol Divers       Date:  2017-01-21       Impact factor: 2.943

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Authors:  Ashutosh Kumar; Kam Y J Zhang
Journal:  J Comput Aided Mol Des       Date:  2018-08-06       Impact factor: 3.686

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Authors:  Andrew Anighoro; Antonio de la Vega de León; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2016-09-21       Impact factor: 3.686

9.  Ligand binding mode prediction by docking: mdm2/mdmx inhibitors as a case study.

Authors:  Nagakumar Bharatham; Kavitha Bharatham; Anang A Shelat; Donald Bashford
Journal:  J Chem Inf Model       Date:  2014-01-21       Impact factor: 4.956

10.  qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X-Ray Electron Density Maps.

Authors:  Gydo C P van Zundert; Brandi M Hudson; Saulo H P de Oliveira; Daniel A Keedy; Rasmus Fonseca; Amelie Heliou; Pooja Suresh; Kenneth Borrelli; Tyler Day; James S Fraser; Henry van den Bedem
Journal:  J Med Chem       Date:  2018-12-06       Impact factor: 7.446

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