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

• Published in: Advanced drug delivery reviews Vol. 86; pp. 2 - 10
• Main Authors: Tian, Sheng, Wang, Junmei, Li, Youyong, Li, Dan, Xu, Lei, Hou, Tingjun
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 23.06.2015
• 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; Traditional Chinese medicines; Computer-aided drug design; Virtual screening; Drug-likeness; ADMET; Machine learning
• ISSN: 0169-409X; 1872-8294; 1872-8294
• DOI: 10.1016/j.addr.2015.01.009

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]