药物数据库
药代动力学
数量结构-活动关系
药物发现
计算机科学
药物开发
数据库
药品
数据挖掘
化学
机器学习
药理学
医学
生物化学
作者
Marielle Rath,James Wellnitz,Holli-Joi Martin,Cleber C. Melo-Filho,Joshua Hochuli,Guilherme Martins Silva,Jon-Michael Beasley,Mark D. Travis,Zoe Sessions,Konstantin Popov,Alexey Zakharov,Artem Cherkasov,Vinícius M. Alves,Eugene Muratov,Alexander Tropsha
标识
DOI:10.1021/acs.jmedchem.3c02446
摘要
Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.
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