期刊
JOURNAL OF MEDICINAL CHEMISTRY
卷 47, 期 18, 页码 4356-4359出版社
AMER CHEMICAL SOC
DOI: 10.1021/jm049970d
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We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.
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