Drug design by machine learning: support vector machines for pharmaceutical data analysis.

R Burbidge; M Trotter; B Buxton; S Holden

doi:10.1016/s0097-8485(01)00094-8

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Drug design by machine learning: support vector machines for pharmaceutical data analysis.

R Burbidge·M Trotter·B Buxton·S Holden

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Abstract

We show that the support vector machine (SVM) classification algorithm, a recent development from the machine learning community, proves its potential for structure-activity relationship analysis. In a benchmark test, the SVM is compared to several machine learning techniques currently used in the field. The classification task involves predicting the inhibition of dihydrofolate reductase by pyrimidines, using data obtained from the UCI machine learning repository. Three artificial neural networks, a radial basis function network, and a C5.0 decision tree are all outperformed by the SVM. The SVM is significantly better than all of these, bar a manually capacity-controlled neural network, which takes considerably longer to train.

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Algorithms Artificial Intelligence Databases, Factual Drug Design Electronic Data Processing Humans Pharmacology, Clinical Structure-Activity Relationship Journal Article Research Support, Non-U.S. Gov't Review

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R Burbidge, M Trotter, B Buxton, S Holden. 2001. Drug design by machine learning: support vector machines for pharmaceutical data analysis.. https://doi.org/10.1016/s0097-8485(01)00094-8

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