Classifying 'drug-likeness' with kernel-based learning methods.
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.
Müller
Klaus-Robert
KR
Fraunhofer FIRST, Kekuléstrasse 7, 12489 Berlin, Germany.
Rätsch
Gunnar
G
Sonnenburg
Sören
S
Mika
Sebastian
S
Grimm
Michael
M
Heinrich
Nikolaus
N
D001185:Artificial Intelligence; D003198:Computer Simulation; D015195:Drug Design; D008956:Models, Chemical