期刊
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
卷 41, 期 5, 页码 1295-1300出版社
AMER CHEMICAL SOC
DOI: 10.1021/ci000397q
关键词
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High-throughput screening has made a significant impact on drug discovery, but there is an acknowledged need for quantitative methods to analyze screening results and predict the activity of further compounds. In this paper we introduce one such method, binary kernel discrimination, and investigate its performance on two datasets; the first is a set of 1650 monoamine oxidase inhibitors, and the second a set of 101437 compounds from an in-house enzyme assay. We compare the performance of binary kernel discrimination with a simple procedure which we call merged similarity search, and also with a feedforward neural network. Binary kernel discrimination is shown to perform robustly with varying quantities of training data and also in the presence of noisy data. We conclude by highlighting the importance of the judicious use of,general pattern recognition techniques for compound selection.
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