GemAffinity: a scoring function for predicting binding affinity and Virtual Screening
Author(s) -
KaiCheng Hsu,
YenFu Chen,
JinnMoon Yang
Publication year - 2012
Publication title -
international journal of data mining and bioinformatics
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.214
H-Index - 21
eISSN - 1748-5681
pISSN - 1748-5673
ISBN - 978-0-7695-3885-3
DOI - 10.1504/ijdmb.2012.045535
Subject(s) - virtual screening , affinities , ligand (biochemistry) , binding affinities , van der waals force , computational biology , binding pocket , function (biology) , drug discovery , protein ligand , chemistry , computer science , computational chemistry , biology , binding site , stereochemistry , genetics , molecule , biochemistry , receptor , organic chemistry
Prediction of protein-ligand binding affinities plays an essential role for molecular recognition and virtual screening. We have developed a scoring function, namely GemAffinity, to predict binding affinities by using a stepwise regression method and 88 descriptors from 891 complex structures. GemAffinity consists of five descriptors, including van der Waals contacts; metal-ligand interactions; water effects; ligand deformation penalty; and conserved hydrogen-bonded residues. Experimental results indicate that GemAffinity is the best among 13 methods on a test set and can enrich screening accuracies on four sets. We believe that GemAffinity is useful for virtual screening and drug discovery.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom