Title: Development and evaluation of a new statistical model for structure-based high-throughput virtual screening

Authors: Shuxing Zhang, Lei Du-Cuny

Addresses: Department of Experimental Therapeutics, The University of Texas M. D. Anderson Cancer Center, Unit 36, Houston, TX 77030, USA. ' Department of Experimental Therapeutics, The University of Texas M. D. Anderson Cancer Center, Unit 36, Houston, TX 77030, USA

Abstract: We have developed a High-Performance Computing (HPC)-based molecular docking scheme, termed HiPCDock, for drug discovery and development. To improve the statistical significance of our screening results, a bioinformatics approach, motivated by a sequence alignment package BLAST, was implemented. The statistical model was validated with ten known Thymidine Kinase (TK) binders and the real inhibitors showed significant statistics, in terms of low probabilities and expectation values. Our HiPCDock has been implemented to be used by both computational experts and experimental scientists. Thus it is an automated, easy-to-use, and efficient package for molecular docking-based high-throughput virtual screening in drug discovery.

Keywords: drug discovery; molecular docking; virtual screening; statistical modelling; HPC; high performance computing; lead identification; bioinformatics; sequence alignment.

DOI: 10.1504/IJBRA.2009.026419

International Journal of Bioinformatics Research and Applications, 2009 Vol.5 No.3, pp.269 - 279

Published online: 11 Jun 2009 *

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