An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation.,Chemical Research in Toxicology

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An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation.
Chemical Research in Toxicology ( IF 3.7 ) Pub Date : 2019-11-22 , DOI: 10.1021/acs.chemrestox.9b00227
Andy H Vo ₁ , Terry R Van Vleet ₁ , Rishi R Gupta ₂ , Michael J Liguori ₁ , Mohan S Rao ₁

Affiliation

Drug toxicity evaluation is an essential process of drug development as it is reportedly responsible for the attrition of approximately 30% of drug candidates. The rapid increase in the number and types of large toxicology data sets together with the advances in computational methods may be used to improve many steps in drug safety evaluation. The development of in silico models to screen and understand mechanisms of drug toxicity may be particularly beneficial in the early stages of drug development where early toxicity assessment can most reduce expenses and labor time. To facilitate this, machine learning methods have been employed to evaluate drug toxicity but are often limited by small and less diverse data sets. Recent advances in machine learning methods together with the rapid increase in big toxicity data such as molecular descriptors, toxicogenomics, and high-throughput bioactivity data may help alleviate some of the current challenges. In this article, the most common machine learning methods used in toxicity assessment are reviewed together with examples of toxicity studies that have used machine learning methodology. Furthermore, a comprehensive overview of the different types of toxicity tools and data sets available to build in silico toxicity prediction models has been provided to give an overview of the current big toxicity data landscape and highlight opportunities and challenges related to them.

中文翻译：

机器学习和药物毒性评估大数据概述。

药物毒性评估是药物开发的必不可少的过程，因为据报道它导致约30％候选药物的消耗。大量毒理学数据集的数量和类型的快速增加以及计算方法的进步可用于改善药物安全性评估的许多步骤。在筛选和了解药物毒性机理的计算机模型的开发中，在药物开发的早期阶段尤其有利，因为早期的毒性评估可以最大程度地减少费用和劳动时间。为了促进这一点，已经采用机器学习方法来评估药物毒性，但是通常受到小的和较少多样性的数据集的限制。机器学习方法的最新进展以及大毒性数据（例如分子描述符，毒理基因组学和高通量生物活性数据可能有助于缓解当前的一些挑战。在本文中，将对毒性评估中最常用的机器学习方法以及使用机器学习方法的毒性研究示例进行综述。此外，还提供了可用于构建计算机毒性预测模型的不同类型的毒性工具和数据集的全面概述，以概述当前的大毒性数据格局，并突出显示了与之相关的机遇和挑战。综述了毒性评估中最常用的机器学习方法，以及使用机器学习方法进行毒性研究的实例。此外，还提供了可用于构建计算机毒性预测模型的不同类型的毒性工具和数据集的全面概述，以概述当前的大毒性数据格局，并重点介绍与之相关的机遇和挑战。综述了毒性评估中最常用的机器学习方法，以及使用机器学习方法进行毒性研究的实例。此外，还提供了可用于构建计算机毒性预测模型的不同类型的毒性工具和数据集的全面概述，以概述当前的大毒性数据格局，并重点介绍与之相关的机遇和挑战。

更新日期：2019-11-28

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