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首页> 外文期刊>Environmental toxicology and chemistry >QUANTITATIVE STRUCTURE―ACTIVITY RELATIONSHIP METHODS: PERSPECTIVES ON DRUG DISCOVERY AND TOXICOLOGY
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QUANTITATIVE STRUCTURE―ACTIVITY RELATIONSHIP METHODS: PERSPECTIVES ON DRUG DISCOVERY AND TOXICOLOGY

机译：定量结构-活性关系方法：药物发现和毒理学的观点

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Quantitative structure―activity relationships (QSARs) attempt to correlate chemical structure with activity using statistical approaches. The QSAR models are useful for various purposes including the prediction of activities of untested chemicals. Quantitative structure―activity relationships and other related approaches have attracted broad scientific interest, particularly in the pharmaceutical industry for drug discovery and in toxicology and environmental science for risk assessment. An assortment of new QSAR methods have been developed during the past decade, most of them focused on drug discovery. Besides advancing our fundamental knowledge of QSARs, these scientific efforts have stimulated their application in a wider range of disciplines, such as toxicology, where QSARs have not yet gained full appreciation. In this review, we attempt to summarize the status of QSAR with emphasis on illuminating the utility and limitations of QSAR technology. We will first review two-dimensional (2D) QSAR with a discussion of the availability and appropriate selection of molecular descriptors. We will then proceed to describe three-dimensional (3D) QSAR and key issues associated with this technology, then compare the relative suitability of 2D and 3D QSAR for different applications. Given the recent technological advances in biological research for rapid identification of drug targets, we mention several examples in which QSAR approaches are employed in conjunction with improved knowledge of the structure and function of the target receptor. The review will conclude by discussing statistical validation of QSAR models, a topic that has received sparse attention in recent years despite its critical importance.
机译：定量结构-活性关系（QSAR）尝试使用统计方法将化学结构与活性相关联。 QSAR模型可用于多种目的，包括预测未经测试的化学品的活性。定量结构-活性关系和其他相关方法引起了广泛的科学兴趣，尤其是在制药业中用于发现药物的研究领域以及在毒理学和环境科学中用于风险评估的领域。在过去的十年中，已经开发出各种新的QSAR方法，其中大多数专注于药物发现。除了提高我们对QSAR的基本知识外，这些科学努力还刺激了它们在更广泛的学科中的应用，例如毒理学，而QSAR尚未得到充分的赞赏。在本文中，我们试图总结QSAR的现状，重点是阐明QSAR技术的实用性和局限性。我们将首先回顾二维（2D）QSAR，并讨论分子描述符的可用性和适当选择。然后，我们将继续描述三维（3D）QSAR和与此技术相关的关键问题，然后比较2D和3D QSAR在不同应用中的相对适用性。鉴于生物学研究中用于快速鉴定药物靶标的最新技术进展，我们提到了几个实例，其中结合了对靶标受体的结构和功能的了解，采用了QSAR方法。审查将通过讨论QSAR模型的统计验证来结束，尽管近年来QSAR模型具有至关重要的意义，但这一话题在最近几年受到了稀疏的关注。

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