Journal of Computer-Aided Molecular Design

, Volume 23, Issue 9, pp 633–644

Robust optimization of scoring functions for a target class

Article

DOI: 10.1007/s10822-009-9276-1

Cite this article as:
Seifert, M.H.J. J Comput Aided Mol Des (2009) 23: 633. doi:10.1007/s10822-009-9276-1

Abstract

Target-specific optimization of scoring functions for protein–ligand docking is an effective method for significantly improving the discrimination of active and inactive molecules in virtual screening applications. Its applicability, however, is limited due to the narrow focus on, e.g., single protein structures. Using an ensemble of protein kinase structures, the publically available directory of useful decoys ligand dataset, and a novel multi-factorial optimization procedure, it is shown here that scoring functions can be tuned to multiple targets of a target class simultaneously. This leads to an improved robustness of the resulting scoring function parameters. Extensive validation experiments clearly demonstrate that (1) virtual screening performance for kinases improves significantly; (2) variations in database content affect this kind of machine-learning strategy to a lesser extent than binary QSAR models, and (3) the reweighting of interaction types is of particular importance for improved screening performance.

Keywords

Virtual screening Scoring function Protein ligand docking Anova Target family Kinase 

Supplementary material

10822_2009_9276_MOESM1_ESM.pdf (2 mb)
Supplementary material 1 (PDF 2011 kb)

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.4SC AGPlanegg-MartinsriedGermany

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