生物信息学
可解释性
计算生物学
计算机科学
药代动力学
药物开发
药物发现
数据科学
生化工程
化学
药理学
机器学习
药品
医学
生物信息学
生物
工程类
基因
生物化学
作者
Emilio S. Petito,David Foster,Michael Ward,Matthew J. Sykes
标识
DOI:10.2174/1568026619666181220105726
摘要
Poor profiles of potential drug candidates, including pharmacokinetic properties, have been acknowledged as a significant hindrance to the development of modern therapeutics. Contemporary drug discovery and development would be incomplete without the aid of molecular modeling (in-silico) techniques, allowing the prediction of pharmacokinetic properties such as clearance, unbound fraction, volume of distribution and bioavailability. As with all models, in-silico approaches are subject to their interpretability, a trait that must be balanced with accuracy when considering the development of new methods. The best models will always require reliable data to inform them, presenting significant challenges, particularly when appropriate in-vitro or in-vivo data may be difficult or time-consuming to obtain. This article seeks to review some of the key in-silico techniques used to predict key pharmacokinetic properties and give commentary on the current and future directions of the field.
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