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题名: Classification of substrates and inhibitors of P-glycoprotein using unsupervised machine learning approach
作者: Wang, YH;  Li, Y;  Yang, SL;  Yang, L
刊名: JOURNAL OF CHEMICAL INFORMATION AND MODELING
发表日期: 2005-05-01
DOI: 10.1021/ci050041k
卷: 45, 期:3, 页:750-757
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology ;  Life Sciences & Biomedicine ;  Physical Sciences ;  Technology
类目[WOS]: Chemistry, Medicinal ;  Chemistry, Multidisciplinary ;  Computer Science, Information Systems ;  Computer Science, Interdisciplinary Applications
研究领域[WOS]: Pharmacology & Pharmacy ;  Chemistry ;  Computer Science
英文摘要: P-glycoprotein (P-gp), a drug efflux pump, affects the bioavailability of therapeutic drugs and plays a potentially important role in clinical drug-drug interactions. Classification of candidate drugs as substrates or inhibitors of the carrier protein is of crucial importance in drug development. Accurate classification is difficult to achieve due to two major factors: i. The extreme diversity of substrates and the presence of multiple binding sites complicate the understanding of the mechanisms behind and hinder the development of a true, conclusive quantitative structure-activity relationship (QSAR) for P-gp substrates. ii. Both inhibitors and substrates interact with the same binding site of P-gp, as a result, it is not surprising that both share many common structural features. In this work, an unsupervised machine learning approach based on the Kohonen self-organizing maps (SOM) was explored, which incorporated a predefined set of physicochemical descriptors encoding the key molecular properties capable of discerning a substrate from an inhibitor. The SOM model can discriminate between substrates and inhibitors with an average accuracy of 82.3%. The current results show that the SOM-based method provides a potential in silico model for virtual screening.
关键词[WOS]: TRANSPORT
语种: 英语
WOS记录号: WOS:000229384000022
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/139724
Appears in Collections:中国科学院大连化学物理研究所_期刊论文

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作者单位: 1.Chinese Acad Sci, Dalian Inst Chem Phys, Grad Sch, Lab Pharmaceut Resource Discovery, Dalian 116023, Peoples R China
2.Dalian Univ Technol, Sch Chem Engn, Dalian 116012, Peoples R China

Recommended Citation:
Wang, YH,Li, Y,Yang, SL,et al. Classification of substrates and inhibitors of P-glycoprotein using unsupervised machine learning approach[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2005,45(3):750-757.
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