Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization

Department of Bioengineering and Therapeutic Sciences, University of California , San Francisco, California 94143-0912, United States.
Journal of Medicinal Chemistry (Impact Factor: 5.45). 10/2012; 55(20):8926-42. DOI: 10.1021/jm301210j
Source: PubMed


Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for "synthesis." Beginning with a small number of molecules, based only on structures and activities, a model was constructed. Compound selection was done computationally, each time making five selections based on confident predictions of high activity and five selections based on a quantitative measure of three-dimensional structural novelty. Compound selection was followed by model refinement using the new data. Iterative computational candidate selection produced rapid improvements in selected compound activity, and incorporation of explicitly novel compounds uncovered much more diverse active inhibitors than strategies lacking active novelty selection.

Full-text preview

Available from:
  • Source
    • "Still another area for exploration is the degree to which, given molecules with low to mid-range potency for training, that the method is able to help “climb the hill” toward more potent molecules. Given the strong dependence on molecular alignment, this may prove to be challenging, though active selection of structurally novel compounds (irrespective of their predicted potency) offers a strategy that was beneficial in the case of bacterial gyrase [17]. With large and diverse data sets that span long time periods becoming increasingly available, we believe that it will be possible to systematically investigate this question. "
    [Show abstract] [Hide abstract]
    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 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.
    Journal of Computer-Aided Molecular Design 11/2013; 27(11). DOI:10.1007/s10822-013-9688-9 · 2.99 Impact Factor
  • Source

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.
    Journal of Medicinal Chemistry 12/2013; 57(12). DOI:10.1021/jm4004285 · 5.45 Impact Factor
Show more