PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

StructRank: A New Approach for Ligand-Based Virtual Screening
Fabian Rathke, Katja Hansen, Ulf Brefeld and Klaus-Robert Müller
Journal of Chemical Information and Modeling Volume 51, Number 1, pp. 83-92, 2011. ISSN 1549-9596


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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7713
Deposited By:Katja Hansen
Deposited On:16 March 2012