Classifying "drug-likeness" with kernel-based learning methods

Müller, K-R; Rätsch, G; Sonnenburg, S; Mika, S; Grimm, M; Heinrich, N

doi:10.1021/ci049737o

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Classifying "drug-likeness" with kernel-based learning methods

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Rätsch, G
Rätsch Group, Friedrich Miescher Laboratory, Max Planck Society;

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Müller, K.-R., Rätsch, G., Sonnenburg, S., Mika, S., Grimm, M., & Heinrich, N. (2005). Classifying "drug-likeness" with kernel-based learning methods. Journal of Chemical Information and Modeling, 45(2), 249-253. doi:10.1021/ci049737o.

Cite as: https://hdl.handle.net/21.11116/0000-000A-DE48-C

Abstract

In this article we report about a successful application of modern machine learning technology, namely Support Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a given set of descriptors 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 chemistry problems that occur in the drug discovery and drug design process.