毒性评价是新药临床前安全性评价的重要内容,这直接关系到人类的健康和毒品的命运。研究如何准确、经济地评价药物毒性具有重要意义。传统的体外和体内毒性试验费力,耗时,非常昂贵,甚至涉及动物福利问题。为药物毒性预测而开发的计算方法可以弥补传统方法的缺点,并且在药物开发的早期阶段被认为是有用的。已经使用各种计算方法开发了许多药物毒性预测模型。随着机器学习和分子表征理论的发展,越来越多的药物毒性预测模型使用各种机器学习方法,如支持向量机,随机森林,天真的贝叶斯,反向传播神经网络。在许多毒性终点方面取得了重大进展,如致癌性,致突变性,和肝毒性。在这次审查中,我们旨在全面概述近年来进行的基于机器学习的药物毒性预测研究。此外,我们在准确性方面比较了这些研究中提出的模型的性能,灵敏度,和特异性,提供了一个视图的当前状态的最先进的在这一领域和突出的问题在当前的研究。
Toxicity evaluation is an important part of the preclinical safety assessment of new drugs, which is directly related to human health and the fate of drugs. It is of importance to study how to evaluate drug toxicity accurately and economically. The traditional in vitro and in vivo toxicity tests are laborious, time-consuming, highly expensive, and even involve animal welfare issues. Computational methods developed for drug toxicity prediction can compensate for the shortcomings of traditional methods and have been considered useful in the early stages of drug development. Numerous drug toxicity prediction models have been developed using a variety of computational methods. With the advance of the theory of machine learning and molecular representation, more and more drug toxicity prediction models are developed using a variety of machine learning methods, such as support vector machine, random forest, naive Bayesian, back propagation neural network. And significant advances have been made in many toxicity endpoints, such as carcinogenicity, mutagenicity, and hepatotoxicity. In this review, we aimed to provide a comprehensive overview of the machine learning based drug toxicity prediction studies conducted in recent years. In addition, we compared the performance of the models proposed in these studies in terms of accuracy, sensitivity, and specificity, providing a view of the current state-of-the-art in this field and highlighting the issues in the current studies.