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

UR - https://www.mendeley.com/catalogue/d3f8c62d-c69f-3a34-bff0-6573ce240dba/

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 -