期刊
ADVANCED DRUG DELIVERY REVIEWS
卷 86, 期 -, 页码 2-10出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.addr.2015.01.009
关键词
Drug-likeness; ADMET; Traditional Chinese medicines; Machine learning; Computer-aided drug design; Virtual screening
资金
- National Natural Science Foundation of China [21173156]
- National Basic Research Program of China (973 program) [2012CB932600]
- Research Fund for the Doctoral Program of Higher Education of China [20123201110017]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
- Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices
- Collaborative Innovation Center of Suzhou Nano Science and Technology
The concept of drug-likeness, established from the analyses of the physiochemical properties or/and structural features of existing small organic drugs or/and drug candidates, has been widely used to filter out compounds with undesirable properties, especially poor ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles. Here, we summarize various approaches for drug-likeness evaluations, including simple rules/filters based on molecular properties/structures and quantitative prediction models based on sophisticated machine learning methods, and provide a comprehensive review of recent advances in this field. Moreover, the strengths and weaknesses of these approaches are briefly outlined. Finally, the drug-likeness analyses of natural products and traditional Chinese medicines (TCM) are discussed. (C) 2015 Elsevier B.V. All rights reserved.
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