Classification scheme for the design of serine protease targeted compound libraries.
The development of a scoring scheme for the classification of molecules into serine protease (SP) actives and inactives is described. The method employed a set of pre-selected descriptors for encoding the molecular structures, and a trained neural network for classifying the molecules. The molecular requirements were profiled and validated by using available databases of SP- and non-SP-active agents [1,439 diverse SP-active molecules, and 5,131 diverse non-SP-active molecules from the Ensemble Database (Prous Science, 2002)] and Sensitivity Analysis. The method enables an efficient qualification or disqualification of a molecule as a potential serine protease ligand. It represents a useful tool for constraining the size of virtual libraries that will help accelerate the development of new serine protease active drugs.
Lang
Stanley A
SA
Chemical Diversity Labs, Inc., 11558 Sorrento Valley Road, San Diego, CA 92121, USA. slang@chemdiv.com
Kozyukov
Andrey V
AV
Balakin
Konstantin V
KV
Skorenko
Andrey V
AV
Ivashchenko
Andrey A
AA
Savchuk
Nikolay P
NP
D003198:Computer Simulation; D016208:Databases, Factual; D015195:Drug Design; D008024:Ligands; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D015842:Serine Proteinase Inhibitors / Q000737:chemistry* / Q000145:classification*
D008024:Ligands; D015842:Serine Proteinase Inhibitors