Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools.
In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanisms, are well suited for predicting ADME and ADME regulatory properties. Recent efforts have been directed at the broadening of application scopes and the improvement of predictive performance with particular focuses on the coverage of ADME properties, and exploration of more diversified training data, appropriate molecular features, and consensus modeling. Moreover, several online machine learning ADME prediction servers have emerged. Here we review these progresses and discuss the performances, application prospects and challenges of exploring machine learning methods as useful tools in predicting ADME and ADME regulatory properties.
['Tao|L|L|', 'Zhang|P|P|', 'Qin|C|C|', 'Chen|S Y|SY|', 'Zhang|C|C|', 'Chen|Z|Z|', 'Zhu|F|F|', 'Yang|S Y|SY|', 'Wei|Y Q|YQ|', 'Chen|Y Z|YZ|']
D003198:Computer Simulation D006801:Humans D000069550:Machine Learning D008954:Models, Biological D004364:Pharmaceutical Preparations D010599:Pharmacokinetics
Machine learning ADME Drug discovery Absorption Distribution Excretion Metabolism Molecular descriptors QSAR