Advances in Machine Learning Prediction of Toxicological Properties and Adverse Drug Reactions of Pharmaceutical Agents

Authors: Ma, Xiao H.; Wang, Rong; Xue, Yin; Li, Ze R.o.n.g.; Yang, Sheng Y.o.n.g.; Wei, Yu Q.u.a.n.; Chen, Yu Z.o.n.g.

Source: Current Drug Safety, Volume 3, Number 2, May 2008 , pp. 100-114(15)

Publisher: Bentham Science Publishers

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Abstract:

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.

Keywords: Statistical learning methods; toxicology; molecular descriptors; structural diversity; adverse effects; risk assessment; drug design; pharmaceutical preparations

Document Type: Research article

Publication date: 2008-05-01

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Current Drug Safety publishes frontier reviews on all the latest advances on drug safety. The journal's aim is to publish the highest quality review articles in the field. Topics covered include: adverse effects of individual drugs and drug classes, management of adverse effects, pharmacovigilance and pharmacoepidemiology of new and existing drugs, post-marketing surveillance. The journal is essential reading for all researchers and clinicians involved in drug safety.