A structure-guided approach for protein pocket modeling and affinity prediction.

ABSTRACT: Binding affinity prediction is frequently addressed using computational models constructed solely with molecular structure and activity data. We present a hybrid structure-guided strategy that combines molecular similarity, docking, and multiple-instance learning such that information from protein structures can be used to inform models of structure-activity relationships. The Surflex-QMOD approach has been shown to produce accurate predictions of binding affinity by constructing an interpretable physical model of a binding site with no experimental binding site structural information. We introduce a method to integrate protein structure information into the model induction process in order to construct more robust physical models. The structure-guided models accurately predict binding aff

SUBMITTER: Varela R 

PROVIDER: S-EPMC3851759 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature