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首页> 外文期刊>Combinatorial Chemistry & High Throughput Screening >The Applications of Machine Learning Algorithms in the Modeling of Estrogen-Like Chemicals
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The Applications of Machine Learning Algorithms in the Modeling of Estrogen-Like Chemicals

机译：机器学习算法在类似雌激素的化学物质建模中的应用

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摘要

Increasing concern is being shown by the scientific community, government regulators, and the public about endocrine-disrupting chemicals that, in the environment, are adversely affecting human and wildlife health through a variety of mechanisms, mainly estrogen receptor-mediated mechanisms of toxicity. Because of the large number of such chemicals in the environment, there is a great need for an effective means of rapidly assessing endocrine-disrupting activity in the toxicology assessment process. When faced with the challenging task of screening large libraries of molecules for biological activity, the benefits of computational predictive models based on quantitative structure-activity relationships to identify possible estrogens become immediately obvious. Recently, in order to improve the accuracy of prediction, some machine learning techniques were introduced to build more effective predictive models. In this review we will focus our attention on some recent advances in the use of these methods in modeling estrogen-like chemicals. The advantages and disadvantages of the machine learning algorithms used in solving this problem, the importance of the validation and performance assessment of the built models as well as their applicability domains will be discussed.
机译：科学界，政府监管机构和公众越来越关注破坏内分泌的化学物质，这些物质在环境中通过多种机制（主要是由雌激素受体介导的毒性机制）对人类和野生生物的健康产生不利影响。由于环境中存在大量此类化学物质，因此迫切需要在毒理学评估过程中快速评估内分泌干扰活性的有效手段。当面对筛选具有生物学活性的大分子文库的艰巨任务时，基于定量结构-活性关系以识别可能的雌激素的计算预测模型的好处就变得显而易见。最近，为了提高预测的准确性，引入了一些机器学习技术来构建更有效的预测模型。在这篇综述中，我们将把注意力集中在使用这些方法模拟雌激素样化学物质方面的最新进展。将讨论用于解决此问题的机器学习算法的优缺点，构建模型的验证和性能评估的重要性以及它们的适用范围。

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