Structrank: A new approach for ligand-based virtual Screening

Fabian Rathke, Katja Hansen, Ulf Brefeld, Klaus Robert Müller

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

Screening large libraries of chemical compounds against a biological target, typically a receptor or an enzyme, is a crucial step in the process of drug discovery. Virtual screening (VS) can be seen as a ranking problem which prefers as many actives as possible at the top of the ranking. As a standard, current Quantitative Structure-Activity Relationship (QSAR) models apply regression methods to predict the level of activity for each molecule and then sort them to establish the ranking. In this paper, we propose a top-k ranking algorithm (StructRank) based on Support Vector Machines to solve the early recognition problem directly. Empirically, we show that our ranking approach outperforms not only regression methods but another ranking approach recently proposed for QSAR ranking, RankSVM, in terms of actives found.

Original languageEnglish
Pages (from-to)83-92
Number of pages10
JournalJournal of Chemical Information and Modeling
Volume51
Issue number1
DOIs
Publication statusPublished - 2011 Jan 24

ASJC Scopus subject areas

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

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