Classifying 'Drug-likeness' with Kernel-Based Learning Methods

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• 作者：Klaus-Robert Mü ; ller ; Gunnar Rä ; tsch ; Sö ; ren Sonnenburg ; Sebastian Mika ; Michael Grimm ; and Nikolaus Heinrich
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2005
• 出版时间：March 2005
• 年：2005
• 卷：45
• 期：2
• 页码：249 - 253
• 全文大小：83K
• 年卷期：v.45,no.2(March 2005)
• ISSN：1549-960X

文摘

In this article we report about a successful application of modern machine learning technology, namelySupport Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a given set ofdescriptors of the substance. We were able to drastically improve the recent result by Byvatov et al. (2003)on this task and achieved an error rate of about 7% on unseen compounds using Support Vector Machines.We see a very high potential of such machine learning techniques for a variety of computational chemistryproblems that occur in the drug discovery and drug design process.