Virtual screening using binary kernel discrimination: analysis of pesticide data.
This paper discusses the use of binary kernel discrimination (BKD) for identifying potential active compounds in lead-discovery programs. BKD was compared with established virtual screening methods in a series of experiments using pesticide data from the Syngenta corporate database. It was found to be superior to methods based on similarity searching and substructural analysis but inferior to a support vector machine. Similar conclusions resulted from application of the methods to a pesticide data set for which categorical activity data were available.
Department of Information Studies, University of Sheffield, Sheffield S10 2TN, UK.
David J
DJ
Wilton
Robert F
RF
Harrison
Peter
P
Willett
John
J
Delaney
Kevin
K
Lawson
Graham
G
Mullier
D000465:Algorithms; D003198:Computer Simulation; D019992:Databases as Topic; D015195:Drug Design; D010575:Pesticides / Q000737:chemistry*; D013329:Structure-Activity Relationship
D010575:Pesticides