Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure–Activity Relationship Models,Journal of Chemical Information and Modeling

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Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure–Activity Relationship Models
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2018-05-29 00:00:00 , DOI: 10.1021/acs.jcim.8b00124
Vinicius M. Alves _{1,

2} , Alexander Golbraikh ₁ , Stephen J. Capuzzi ₁ , Kammy Liu ₃ , Wai In Lam ₃ , Daniel Robert Korn ₃ , Diane Pozefsky ₃ , Carolina Horta Andrade ₂ , Eugene N. Muratov _{1,

4} , Alexander Tropsha ₁

Affiliation

Multiple approaches to quantitative structure–activity relationship (QSAR) modeling using various statistical or machine learning techniques and different types of chemical descriptors have been developed over the years. Oftentimes models are used in consensus to make more accurate predictions at the expense of model interpretation. We propose a simple, fast, and reliable method termed Multi-Descriptor Read Across (MuDRA) for developing both accurate and interpretable models. The method is conceptually related to the well-known kNN approach but uses different types of chemical descriptors simultaneously for similarity assessment. To benchmark the new method, we have built MuDRA models for six different end points (Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG liability, skin sensitization, and endocrine disruption) and compared the results with those generated with conventional consensus QSAR modeling. We find that models built with MuDRA show consistently high external accuracy similar to that of conventional QSAR models. However, MuDRA models excel in terms of transparency, interpretability, and computational efficiency. We posit that due to its methodological simplicity and reliable predictive accuracy, MuDRA provides a powerful alternative to a much more complex consensus QSAR modeling. MuDRA is implemented and freely available at the Chembench web portal (https://chembench.mml.unc.edu/mudra).

中文翻译：

跨多描述符读取（MuDRA）：开发准确的定量结构-活动关系模型的简单透明方法

多年来，已经开发出了多种使用多种统计或机器学习技术以及不同类型的化学描述符进行定量构效关系（QSAR）建模的方法。通常，以协商一致的方式使用模型，以牺牲模型解释为代价做出更准确的预测。我们提出了一种简单，快速且可靠的方法，称为“多描述符跨读（MuDRA）”，用于开发准确且可解释的模型。该方法在概念上与众所周知的kNN方法有关，但同时使用不同类型的化学描述符进行相似性评估。为了对新方法进行基准测试，我们针对六个不同的终点（Ames致突变性，水生毒性，肝毒性，hERG敏感性，皮肤致敏性，和内分泌干扰），并将结果与传统的共识QSAR建模结果进行比较。我们发现，使用MuDRA构建的模型显示出与传统QSAR模型相似的始终如一的高外部精度。但是，MuDRA模型在透明度，可解释性和计算效率方面表现出色。我们认为，由于其方法的简单性和可靠的预测准确性，MuDRA提供了更复杂的共识QSAR建模的有力替代方案。MuDRA已在Chembench网站（https://chembench.mml.unc.edu/mudra）上实现并免费提供 MuDRA模型在透明度，可解释性和计算效率方面表现出色。我们认为，由于其方法的简单性和可靠的预测准确性，MuDRA提供了更复杂的共识QSAR建模的有力替代方案。MuDRA已在Chembench网站（https://chembench.mml.unc.edu/mudra）上实现并免费提供 MuDRA模型在透明度，可解释性和计算效率方面表现出色。我们认为，由于其方法的简单性和可靠的预测准确性，MuDRA提供了更复杂的共识QSAR建模的有力替代方案。MuDRA已在Chembench网站（https://chembench.mml.unc.edu/mudra）上实现并免费提供）。

更新日期：2018-05-29

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