Journal of Computer-Aided Molecular Design

, Volume 27, Issue 11, pp 917-934

Open Access This content is freely available online to anyone, anywhere at any time.

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

  • Rocco VarelaAffiliated withCertara L.P
  • , Ann E. ClevesAffiliated withHelen Diller Family Comprehensive Cancer Center, University of California, San Francisco
  • , Russell SpitzerAffiliated withDataStax
  • , Ajay N. JainAffiliated withDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco Email author 


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 affinities over a broad range of compounds while producing more accurate representations of the protein pockets and ligand binding modes. Structure-guidance for the QMOD method yielded significant performance improvements, both for affinity and pose prediction, especially in cases where predictions were made on ligands very different from those used for model induction.


QMOD QSAR Surflex MM-PBSA Affinity prediction Random forest