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Customizing scoring functions for docking

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Abstract

Empirical scoring functions used in protein-ligand docking calculations are typically trained on a dataset of complexes with known affinities with the aim of generalizing across different docking applications. We report a novel method of scoring-function optimization that supports the use of additional information to constrain scoring function parameters, which can be used to focus a scoring function’s training towards a particular application, such as screening enrichment. The approach combines multiple instance learning, positive data in the form of ligands of protein binding sites of known and unknown affinity and binding geometry, and negative (decoy) data of ligands thought not to bind particular protein binding sites or known not to bind in particular geometries. Performance of the method for the Surflex-Dock scoring function is shown in cross-validation studies and in eight blind test cases. Tuned functions optimized with a sufficient amount of data exhibited either improved or undiminished screening performance relative to the original function across all eight complexes. Analysis of the changes to the scoring function suggest that modifications can be learned that are related to protein-specific features such as active-site mobility.

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Acknowledgements

The authors gratefully acknowledge NIH for partial funding of the work (grant GM070481). Dr. Jain has a financial interest in BioPharmics LLC, a biotechnology company whose main focus is in the development of methods for computational modeling in drug discovery. Tripos Inc., has exclusive commercial distribution rights for Surflex-Dock, licensed from BioPharmics LLC.

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  1. University of California, San Francisco, Box 0128, San Francisco, CA, 94143-0128, USA

    Tuan A. Pham & Ajay N. Jain

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  1. Tuan A. Pham
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  2. Ajay N. Jain
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Correspondence to Ajay N. Jain.

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Pham, T.A., Jain, A.N. Customizing scoring functions for docking. J Comput Aided Mol Des 22, 269–286 (2008). https://doi.org/10.1007/s10822-008-9174-y

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