Virtual screening of molecular databases using a support vector machine

Robert N. Jorissen, Michael K. Gilson

Research output: Contribution to journalArticlepeer-review

215 Citations (Scopus)


The Support Vector Machine (SVM) is an algorithm that derives a model used for the classification of data into two categories and which has good generalization properties. This study applies the SVM algorithm to the problem of virtual screening for molecules with a desired activity. In contrast to typical applications of the SVM, we emphasize not classification but enrichment of actives by using a modified version of the standard SVM function to rank molecules. The method employs a simple and novel criterion for picking molecular descriptors and uses cross-validation to select SVM parameters. The resulting method is more effective at enriching for active compounds with novel chemistries than binary fingerprint-based methods such as binary kernel discrimination.

Original languageEnglish
Pages (from-to)549-561
JournalJournal of Chemical Information and Modeling
Issue number3
Publication statusPublished or Issued - May 2005
Externally publishedYes

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences

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