Literature DB >> 23244516

How to improve docking accuracy of AutoDock4.2: a case study using different electrostatic potentials.

Xuben Hou1, Jintong Du, Jian Zhang, Lupei Du, Hao Fang, Minyong Li.   

Abstract

Molecular docking, which is the indispensable emphasis in predicting binding conformations and energies of ligands to receptors, constructs the high-throughput virtual screening available. So far, increasingly numerous molecular docking programs have been released, and among them, AutoDock 4.2 is a widely used docking program with exceptional accuracy. It has heretofore been substantiated that the calculation of partial charge is very fundamental for the accurate conformation search and binding energy estimation. However, no systematic comparison of the significances of electrostatic potentials on docking accuracy of AutoDock 4.2 has been determined. In this paper, nine different charge-assigning methods, including AM1-BCC, Del-Re, formal, Gasteiger-Hückel, Gasteiger-Marsili, Hückel, Merck molecular force field (MMFF), and Pullman, as well as the ab initio Hartree-Fock charge, were sufficiently explored for their molecular docking performance by using AutoDock4.2. The results clearly demonstrated that the empirical Gasteiger-Hückel charge is the most applicable in virtual screening for large database; meanwhile, the semiempirical AM1-BCC charge is practicable in lead compound optimization as well as accurate virtual screening for small databases.

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Year:  2013        PMID: 23244516     DOI: 10.1021/ci300417y

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


  20 in total

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7.  Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery.

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8.  Laser irradiated phenothiazines: New potential treatment for COVID-19 explored by molecular docking.

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10.  6-Shogaol induces apoptosis in human leukemia cells through a process involving caspase-mediated cleavage of eIF2α.

Authors:  Qun Liu; Yong-Bo Peng; Ping Zhou; Lian-Wen Qi; Mu Zhang; Ning Gao; E-Hu Liu; Ping Li
Journal:  Mol Cancer       Date:  2013-11-12       Impact factor: 27.401

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