用于定量构效关系研究的机器学习算法作为药物发现的新方法

Mourad Stitou, H. Toufik, M. Bouachrine, H. Bih, F. Lamchouri
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引用次数: 2

摘要

开发机器学习算法已经成为药物发现过程中的重要工具。目前,各种机器学习工具被用于定量构效关系(quantitative structure-activity relations, QSAR)中,以建立QSAR模型。2D-QSAR分析涉及通过使用机器学习算法(如偏最小二乘法(PLS)和人工神经网络(ann))研究分子描述符与生物活性之间的定量关系。通过偏最小二乘法(PLS)建立的最佳线性2D-QSAR模型具有较高的预测能力(R2 = 0.87, F=52.80, R2 = 0.80, Q2 = 0.77)。采用Levenberge Marquardt (L-M)算法(体系结构[3-3-1]:R2=0.94, R2pred=0.81, Q2=0.86)的非线性人工神经网络(ann)表现出更好的性能。这些结果揭示了a_nO, PEOE_VSA+6和Vsurf_R是生物活性所依赖的重要描述符。其中,保留的3D-QSAR模型效果最好(R2 = 0.94, R2 = 0.80, Q2 = 0.67)。然而,通过3D-QSAR分析获得的衍生等高线图显示了可以提高细胞毒活性的有利和不利区域。因此,基于机器学习方法建立的QSAR模型可以帮助我们了解设计具有改进生物活性的新化合物所需的结构要求。
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 algorithms used in Quantitative structure-activity relationships studies as new approaches in drug discovery
Developing machine learning algorithms have become important tools in drug discovery process. Nowadays, a variety of machine learning tools are used in quantitative structure-activity relationships (QSARs) to establish QSAR models. The 2D-QSAR analysis involves the study of quantitative relationships between the molecular descriptors and biological activity by using machine learning algorithms, such as partial least squares (PLS) and artificial neural networks (ANNs). The best linear 2D-QSAR model was developed through partial least squares (PLS) gave a high predictive ability (R2 = 0.87, F=52.80, R2pred = 0.80, Q2 = 0.77). Moreover, the non-linear artificial neural networks (ANNs) was shown better performance with Levenberge Marquardt (L-M) algorithm (architecture [3-3-1]: R2=0.94, R2pred=0.81, Q2=0.86). Those results uncovered that a_nO, PEOE_VSA+6 and Vsurf_R are important descriptors on which biological activity depends. Moreover, the retained 3D-QSAR model exhibits the best results (R2 = 0.94, R2pred = 0.80, Q2 = 0.67). However, the derived contour maps obtained by 3D-QSAR analysis indicate the favorable and unfavorable regions that could improve the cytotoxic activity. As a consequence, the established QSAR models based on machine learning methods could help us to understand the structural requirements necessary to design new compounds with improved biological activity.
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