StructRank: a new approach for ligand-based virtual screening
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 standa...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2011
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| In: |
Journal of chemical information and modeling
Year: 2011, Volume: 51, Issue: 1, Pages: 83-92 |
| ISSN: | 1549-960X |
| DOI: | 10.1021/ci100308f |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1021/ci100308f Verlag, PURE Lüneburg: http://fox.leuphana.de/portal/de/publications/structrank(47ad8a63-e998-4cb2-a744-9aed3adadbb2).html 
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| Author Notes: | Fabian Rathke, Katja Hansen, Ulf Brefeld, and Klaus-Robert Müller |
| Summary: | 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. |
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| Item Description: | Published online17 December 2010 Gesehen am 21.09.2022 |
| Physical Description: | Online Resource |
| ISSN: | 1549-960X |
| DOI: | 10.1021/ci100308f |