In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure–activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naïve Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.

中文翻译：

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预测毒理学中的机器学习：分类模型的最新应用和未来方向

近年来，机器学习在预测毒理学中变得越来越重要，因为它已经从体内研究转向了计算机研究。目前，正在使用体外方法以及其他计算方法，例如定量构效关系建模和吸收、分布、代谢和排泄计算。这里概述了机器学习及其在预测毒理学中的应用，包括支持向量机 (SVM)、随机森林 (RF) 和决策树 (DT)、神经网络、回归模型、朴素贝叶斯、k-最近的邻居和集成学习。总结了这些机器学习方法在预测毒理学方面的最新成功，并介绍了预测毒理学中使用的一些模型的比较。在预测毒理学中，由于可用数据的特性，SVM、RF 和 DT 是主要的机器学习方法。最后，这篇综述描述了在预测毒理学中使用机器学习面临的当前挑战，并提供了对该领域可能的改进领域的见解。