Development and evaluation of a new statistical model for structure-based high-throughput virtual screening.
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.
Zhang
Shuxing
S
Department of Experimental Therapeutics, The University of Texas M. D. Anderson Cancer Center, Unit 36, Houston, TX 77030, USA. shuzhang@mdanderson.org
Du-Cuny
Lei
L
D019295:Computational Biology / Q000379:methods; D015195:Drug Design; D055808:Drug Discovery / Q000379:methods*; D015233:Models, Statistical; D047428:Protein Kinase Inhibitors / Q000737:chemistry; D013329:Structure-Activity Relationship; D013937:Thymidine Kinase / Q000037:antagonists & inhibitors
D047428:Protein Kinase Inhibitors; D013937:Thymidine Kinase