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243rd ACS
Mar 25-29, 2012
San Diego, CA
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Presentation at the:  eCheminfo Autumn Community of Practice meeting
  Oct 13-16, 2009, Bryn Mawr, PA, USA

WEDNESDAY 14 OCTOBER 2009: Structure-Based Drug Design

Chaired by Natasja Brooijmans (Wyeth)

Improving Molecular Docking Through a Tunable Scoring Function

Zsolt Zsoldos , Orr Ravitz
, Danni Harris and Aniko Simon

SimBioSys, Inc., 135 Queen's Plate Dr., Unit 520, Toronto, ON, M9W 6V1, Canada


The molecular docking paradigm, has thus far failed to produce a generic approach that would deliver accurate pose prediction capabilities, and reliable rank-ordering of conformations and ligands consistently for any biological system of interest. This reality, which has been addressed by numerous methodology papers and comparative studies, has been largely attributed to the inability of scoring functions to capture different chemical interaction types at a uniform level of accuracy. Several studies attempted to develop guidelines for choosing the most suitable docking and scoring method for a specific problem based on protein family classification of the target, dominant interactions, and other properties of the studied system. Consensus techniques, on the other hand, try to synergistically integrate information from multiple sources assuming agreement between different methods is indicative of more accurate values. Both approaches, however, have shown only limited success in improving binding mode and activity prediction capabilities.

An alternative solution, and arguably a more rigorous one, would be to tailor the scoring function for the system of interest. eHiTS uses a novel scoring method consisting of a statistical knowledge base focused on interacting surface points and physical terms combined with an adaptive parameter scheme. This approach offers users the capability to fine-tune the scoring function using their data and thus incorporate their full body of knowledge in a systematic and automatic fashion. In many realistic drug discovery scenarios, structural and ligand-activity information is sufficient in a statistical sense to adjust a limited set of parameters representing the relative weights of the various terms in the eHiTS scoring function. During tuning, receptor targets are clustered according to the chemical and shape similarity of the active site, and weight sets are optimized for each family. Pharmacophore constraint descriptions are thus generated automatically from the recurring interaction patterns observed in a specific active set profile. These constraints can be used for constrained docking or pharmacophore-enhanced scoring schemes.

In this talk, an overview of the eHiTS' tuning utility will be given, outlining the underlying methodology. Acetylcholine binding protein, beta secretase and other systems of pharmaceutical interest will be used to demonstrate the improvement in docking performance in terms of score discrimination between low and high RMSD poses, of enrichment levels in screening runs, and of correlation between score and binding affinity. Guidelines for choosing the optimal data set for training will be discussed.

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