TY  - JOUR
A1  - Muller, K. R.
A1  - Ratsch, G.
A1  - Sonnenburg, S.
A1  - Mika, Sebastian
A1  - Grimm, M.
A1  - Heinrich, N.
T1  - Classifying 'drug-likeness' with kernel-based learning methods
N2  - 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
Y1  - 2005
SN  - 1549-9596
ER  -