The application of in silico drug-likeness predictions in pharmaceutical research

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, m...

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Bibliographic Details
Published in:Advanced drug delivery reviews 2015-06, Vol.86, p.2-10
Main Authors: Tian, Sheng, Wang, Junmei, Li, Youyong, Li, Dan, Xu, Lei, Hou, Tingjun
Format: Article
Language:English
Subjects:
ADMET
Biological Products
Biomedical Research
Computer Simulation
Computer-aided drug design
Drug Discovery
Drug-likeness
Humans
Machine Learning
Medicine, Chinese Traditional
Models, Biological
Molecular Structure
Pharmaceutical Preparations - chemistry
Pharmaceutical Preparations - metabolism
Pharmacokinetics
Traditional Chinese medicines
Virtual screening
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Summary: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. [Display omitted]
ISSN:0169-409X
1872-8294
DOI:10.1016/j.addr.2015.01.009