Support Vector Machine (SVM) Models for Predicting Inhibitors of the 3′ Processing Step of HIV-1 Integrase-Reference-Cited by-同舟云学术

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Support Vector Machine (SVM) Models for Predicting Inhibitors of the 3′ Processing Step of HIV-1 Integrase

Published:2013-09-09 Issue:9-10 Volume:32 Page:811-826
ISSN:1868-1743
Container-title:Molecular Informatics
language:en
Short-container-title:Mol. Inf.

Author:

Xuan Shouyi,Wang Maolin,Kang Hang,Kirchmair Johannes,Tan Lu,Yan Aixia

Funder

National Natural Science Foundation of China

“Chemical Grid Project” of Beijing University of Chemical Technology

Molecular Networks GmbH, Erlangen, Germany

Publisher

Wiley

Subject

Organic Chemistry,Computer Science Applications,Drug Discovery,Molecular Medicine,Structural Biology

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/minf.201300107/fullpdf

Reference98 articles.

1. 2013

2. Pharmacophore-Based Design of HIV-1 Integrase Strand-Transfer Inhibitors

3. Design of a series of bicyclic HIV-1 integrase inhibitors. Part 2: Azoles: Effective metal chelators

4. HIV chemotherapy

5. Drug resistance profiles for the HIV integrase gene in patients failing raltegravir salvage therapy*

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Quantitative Structure-Activity Relationship Study for HIV-1 LEDGF/p75 Inhibitors;Current Computer-Aided Drug Design;2020-11-09

2. Filtered circular fingerprints improve either prediction or runtime performance while retaining interpretability;Journal of Cheminformatics;2016-10-31

3. Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine;Combinatorial Chemistry & High Throughput Screening;2016-06-09

4. Targeting HIV-1 integrase with strand transfer inhibitors;Drug Discovery Today;2015-04

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