Structrank: A new approach for ligand-based virtual Screening
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Structrank : A new approach for ligand-based virtual Screening. / Rathke, Fabian; Hansen, Katja; Brefeld, Ulf et al.
In: Journal of Chemical Information and Modeling, Vol. 51, No. 1, 24.01.2011, p. 83-92.Research output: Journal contributions › Journal articles › Research › peer-review
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RIS
TY - JOUR
T1 - Structrank
T2 - A new approach for ligand-based virtual Screening
AU - Rathke, Fabian
AU - Hansen, Katja
AU - Brefeld, Ulf
AU - Müller, Klaus Robert
PY - 2011/1/24
Y1 - 2011/1/24
N2 - 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.
AB - 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.
KW - Informatics
KW - Biological targets
KW - Drug discovery
KW - New approaches
KW - Quantitative structure-activity relationships
KW - Ranking algorithm
KW - Ranking approach
KW - Ranking problems
KW - Regression method
KW - Virtual Screening
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=79952593481&partnerID=8YFLogxK
U2 - 10.1021/ci100308f
DO - 10.1021/ci100308f
M3 - Journal articles
C2 - 21166393
AN - SCOPUS:79952593481
VL - 51
SP - 83
EP - 92
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
SN - 1549-9596
IS - 1
ER -