title: The application of in silico drug-likeness predictions in pharmaceutical research.
abstract: 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.
authors: College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu 215123, China.
authors: Sheng
authors: S
authors: Tian
authors: Green Center for Systems Biology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, United States.
authors: Junmei
authors: J
authors: Wang
authors: Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China.
authors: Youyong
authors: Y
authors: Li
authors: College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
authors: Dan
authors: D
authors: Li
authors: College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
authors: Lei
authors: L
authors: Xu
authors: College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu 215123, China. Electronic address: tingjunhou@zju.edu.cn.
authors: Tingjun
authors: T
authors: Hou
mesh_terms: D001688:Biological Products; D035843:Biomedical Research; D003198:Computer Simulation; D055808:Drug Discovery; D006801:Humans; D000069550:Machine Learning; D008516:Medicine, Chinese Traditional; D008954:Models, Biological; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000378:metabolism; D010599:Pharmacokinetics
chemical_list: D001688:Biological Products; D004364:Pharmaceutical Preparations
keywords: ADMET; Computer-aided drug design; Drug-likeness; Machine learning; Traditional Chinese medicines; Virtual screening