title: StructRank: a new approach for ligand-based virtual screening.
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.
authors.affiliation: Department of Machine Learning, University of Technology, Berlin, Germany. fabian.rathke@iwr.uni-heidelberg.de
authors.forename: Fabian
authors.initials: F
authors.lastname: Rathke
authors.forename: Katja
authors.initials: K
authors.lastname: Hansen
authors.forename: Ulf
authors.initials: U
authors.lastname: Brefeld
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D001185:Artificial Intelligence; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D004791:Enzyme Inhibitors / Q000737:chemistry / Q000494:pharmacology; D008024:Ligands; D012044:Regression Analysis; D013329:Structure-Activity Relationship; D013997:Time Factors; D014584:User-Computer Interface
chemical_list: D004791:Enzyme Inhibitors; D008024:Ligands