title: Training a scoring function for the alignment of small molecules.
abstract: 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.
authors.affiliation: Chemical Computing Group, Inc., Montreal, H3A 2R7, Canada. slchan3@yahoo.com
authors.forename: Shek Ling
authors.initials: SL
authors.lastname: Chan
authors.forename: Paul
authors.initials: P
authors.lastname: Labute