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Training a Scoring Function for the Alignment of Small Molecules
Author(s) -
S.L.-F. Chan,
Paul Labute
Publication year - 2010
Publication title -
journal of chemical information and modeling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 160
eISSN - 1549-960X
pISSN - 1549-9596
DOI - 10.1021/ci100227h
Subject(s) - function (biology) , training set , set (abstract data type) , space (punctuation) , molecule , small molecule , computer science , protein data bank , chemical space , hydrogen bond , data set , protein function , biological system , chemical physics , chemistry , data mining , artificial intelligence , protein structure , biology , drug discovery , biochemistry , evolutionary biology , organic chemistry , gene , programming language , operating system
A comprehensive data set of aligned ligands with highly similar binding pockets from the Protein Data Bank has been built. Based on this data set, a scoring function for recognizing good alignment poses for small molecules has been developed. This function is based on atoms and hydrogen-bond projected features. The concept is simply that atoms and features of a similar type (hydrogen-bond acceptors/donors and hydrophobic) tend to occupy the same space in a binding pocket and atoms of incompatible types often tend to avoid the same space. Comparison with some recently published results of small molecule alignments shows that the current scoring function can lead to performance better than those of several existing methods.

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