Journal of Chemical Information and Modeling · 2006 · 44 citations · 13 references
PharmacyData ScienceToxicologyData MiningDrug DiscoveryMachine LearningKernel MethodsLead DiscoveryLibrary ScreeningMolecular SimilaritySupport Vector MachinesVirtual ScreeningCategorical Data RepresentationChemical Similarity AssessmentChemical Similarity SearchChemical Structure ClassificationSimilarity SearchingSupport Vector Machine ClassificationVirtual Screening BenchmarkingVirtual Screening Validation
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.
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