Advances in machine learning prediction of toxicological properties and adverse drug reactions of pharmaceutical agents.
As part of the intensive efforts in facilitating drug discovery, computational methods have been explored as low-cost and efficient tools for predicting various toxicological properties and adverse drug reactions (ADR) of pharmaceutical agents. More recently, machine learning methods have been applied for developing tools capable of predicting diverse spectrum of compounds of different toxicological properties and ADR profiles. Based on the results of a number of studies, these methods have shown promising potential in predicting a variety of toxicological properties and ADR profiles. This article reviews the strategies, current progresses, underlying difficulties and future prospects in using machine learning methods for predicting compounds of specific toxicological property or ADR profile.
Ma
Xiao Hua
XH
Bioinformatics and Drug Design Group, Department of Computational Science, National University of Singapore, 3 Science Drive 2, Singapore.
Wang
Rong
R
Xue
Yin
Y
Li
Ze Rong
ZR
Yang
Sheng Yong
SY
Wei
Yu Quan
YQ
Chen
Yu Zong
YZ
D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003661:Decision Support Techniques; D003663:Decision Trees; D006801:Humans; D016015:Logistic Models; D008958:Models, Molecular; D015394:Molecular Structure; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D013329:Structure-Activity Relationship; D018675:Toxicity Tests / Q000379:methods*