毒理学中的机器学习:评估药物毒性的机遇

L. Tonoyan, Arno G. Siraki
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引用次数: 0

摘要

毒理学中的机器学习(ML)正呈指数级增长,这带来了前所未有的机遇,也为在这一领域使用 ML 带来了重要的考虑因素。本综述讨论了监督学习、无监督学习和强化学习及其在毒理学中的应用。科学方法的应用是开发 ML 模型的核心。这些步骤包括定义 ML 问题、构建数据集、转换数据和特征选择、选择和训练 ML 模型、验证和预测。由于化学品及其与生物群的相互作用种类繁多,对严格模型的要求也越来越高。大型数据集使这项任务成为可能,但选择具有重叠化学空间的数据库也是一个重要的考虑因素。通过机器学习预测毒性可以产生重大的社会影响,包括增强风险评估、确定临床毒性、评估致癌特性以及检测药物的有害副作用。我们将简明扼要地概述这一课题的现状,重点关注与大量数据集的可用性相关的潜在优势和挑战、分析这些数据集的方法以及应用此类模型所涉及的伦理问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning in toxicological sciences: opportunities for assessing drug toxicity
Machine learning (ML) in toxicological sciences is growing exponentially, which presents unprecedented opportunities and brings up important considerations for using ML in this field. This review discusses supervised, unsupervised, and reinforcement learning and their applications to toxicology. The application of the scientific method is central to the development of a ML model. These steps involve defining the ML problem, constructing the dataset, transforming the data and feature selection, choosing and training a ML model, validation, and prediction. The need for rigorous models is becoming more of a requirement due to the vast number of chemicals and their interaction with biota. Large datasets make this task possible, though selecting databases with overlapping chemical spaces, amongst other things, is an important consideration. Predicting toxicity through machine learning can have significant societal impacts, including enhancements in assessing risks, determining clinical toxicities, evaluating carcinogenic properties, and detecting harmful side effects of medications. We provide a concise overview of the current state of this topic, focusing on the potential benefits and challenges related to the availability of extensive datasets, the methodologies for analyzing these datasets, and the ethical implications involved in applying such models.
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