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A New, Improved Hybrid Scoring Function for Molecular Docking and Scoring Based on AutoDock and AutoDock Vina
Author(s) -
Tanchuk Vsevolod Yu,
Tanin Volodymyr O.,
Vovk Andriy I.,
Poda Gennady
Publication year - 2016
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.12697
Subject(s) - autodock , test set , computer science , mean squared error , docking (animal) , machine learning , artificial intelligence , mathematics , statistics , chemistry , in silico , medicine , biochemistry , nursing , gene
Automated docking is one of the most important tools for structure‐based drug design that allows prediction of ligand binding poses and also provides an estimate of how well small molecules fit in the binding site of a protein. A new scoring function based on AutoDock and AutoDock Vina has been introduced. The new hybrid scoring function is a linear combination of the two scoring function components derived from a multiple linear regression fitting procedure. The scoring function was built on a training set of 2412 protein–ligand complexes from pdbbind database ( www.pdbbind.org.cn , version 2012). A test set of 313 complexes that appeared in the 2013 version was used for validation purposes. The new hybrid scoring function performed better than the original functions, both on training and test sets of protein–ligand complexes, as measured by the non‐parametric Pearson correlation coefficient, R , mean absolute error (MAE), and root‐mean‐square error (RMSE) between the experimental binding affinities and the docking scores. The function also gave one of the best results among more than 20 scoring functions tested on the core set of the pdbbind database. The new AutoDock hybrid scoring function will be implemented in modified version of AutoDock.