Department of Computer Engineering, Alzahra University, Tehran, Iran.
Data Mining Research Laboratory, Department of Computer Engineering, Alzahra University, Tehran, Iran.
Curr Drug Discov Technol. 2021;18(1):17-30. doi: 10.2174/1570163817666200316104404.
Quantitative Structure-Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to the significant role of machine learning strategies in QSAR modeling, this area of research has attracted much attention from researchers. A considerable amount of literature has been published on machine learning based QSAR modeling methodologies whilst this domain still suffers from lack of a recent and comprehensive analysis of these algorithms. This study systematically reviews the application of machine learning algorithms in QSAR, aiming to provide an analytical framework. For this purpose, we present a framework called 'ML-QSAR'. This framework has been designed for future research to: a) facilitate the selection of proper strategies among existing algorithms according to the application area requirements, b) help to develop and ameliorate current methods and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a structured categorization is depicted which studied the QSAR modeling research based on machine models. Then several criteria are introduced in order to assess the models. Finally, inspired by aforementioned criteria the qualitative analysis is carried out.
定量构效关系 (QSAR) 是一种流行的方法，旨在根据化学结构将化学分子与其生物活性相关联。机器学习技术已被证明是 QSAR 建模的有前途的解决方案。由于机器学习策略在 QSAR 建模中的重要作用，该研究领域引起了研究人员的极大关注。已经发表了大量关于基于机器学习的 QSAR 建模方法的文献，而该领域仍然缺乏对这些算法的最新和全面分析。本研究系统地回顾了机器学习算法在 QSAR 中的应用，旨在提供一个分析框架。为此，我们提出了一个称为“ML-QSAR”的框架。该框架旨在为未来的研究提供：a）根据应用领域的要求，在现有算法中选择合适的策略，b）帮助开发和改进当前的方法，c）提供一个比较研究现有方法的平台。在 ML-QSAR 中，首先描述了一个基于机器模型的 QSAR 建模研究的结构化分类。然后引入了几个标准来评估模型。最后，受上述标准的启发，进行了定性分析。
