Prev. Article Next Article Table of Contents

Article

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

Klaus-Robert Müller,^†^‡ Gunnar Rätsch,^§ Sören Sonnenburg,*^† Sebastian Mika, Michael Grimm, and Nikolaus Heinrich

Fraunhofer FIRST, Kekulstrasse 7, 12489 Berlin, Germany, Computer Science, University of Potsdam, August-Bebel-Strasse 89, 14482 Potsdam, Germany, Friedrich Miescher Laboratory of the Max Planck Society, Spemannstrasse 39, 72076 Tbingen, Germany, idalab GmbH, Mohrenstrasse 63, 10117 Berlin, Germany, and Computational Chemistry, Schering AG, Mllerstrasse 178, 13342 Berlin, Germany

J. Chem. Inf. Model., 2005, 45 (2), pp 249–253

DOI: 10.1021/ci049737o

Publication Date (Web): February 8, 2005

Copyright © 2005 American Chemical Society

†

Fraunhofer FIRST.

,

‡

University of Potsdam.

,

§

Friedrich Miescher Laboratory of the Max Planck Society.

,

*

Corresponding author e-mail: soeren.sonnenburg@first.fraunhofer.de.

,

idalab GmbH.

,

Schering AG.

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.

View: Full Text HTML | Hi-Res PDF

Tools

SciFinder Links

Get Reference Detail

History

Published In Issue March 28, 2005
Received August 24, 2004

Recommend & Share

Related Content

Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug ClassificationJournal of Chemical Information and Computer Sciences
- Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification
  Evgeny Byvatov, Uli Fechner, Jens Sadowski, and Gisbert Schneider
  Journal of Chemical Information and Computer Sciences 2003 43 (6), pp 1882–1889
  Abstract: Support vector machine (SVM) and artificial neural network (ANN) systems were applied to a drug/nondrug classification problem as an example of binary decision problems in early-phase virtual compound filtering and screening. The results indicate that ...
  Abstract | Full Text HTML | Hi-Res PDF
Drug Discovery Using Support Vector Machines. The Case Studies of Drug-likeness, Agrochemical-likeness, and Enzyme Inhibition PredictionsJournal of Chemical Information and Computer Sciences
- Drug Discovery Using Support Vector Machines. The Case Studies of Drug-likeness, Agrochemical-likeness, and Enzyme Inhibition Predictions
  Vladimir V. Zernov, Konstantin V. Balakin, Andrey A. Ivaschenko, Nikolay P. Savchuk, and Igor V. Pletnev
  Journal of Chemical Information and Computer Sciences 2003 43 (6), pp 2048–2056
  Abstract: Support Vector Machines (SVM) is a powerful classification and regression tool that is becoming increasingly popular in various machine learning applications. We tested the ability of SVM, in comparison with well-known neural network techniques, to ...
  Abstract | Full Text HTML | Hi-Res PDF

Other ACS content by these authors:

Copyright © 2009 American Chemical Society

1155 Sixteenth Street N.W., Washington, DC 20036

Journals A–Z| Books| Authors & Reviewers| Librarians| ACS Members| Help

Portico

COUNTER

CrossRef

ACS Publications is a partner of:

Technology Partner - Atypon Systems, Inc.