3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models

Abstract Quantitative Structure-Activity Relationship (QSAR) is a computer-aided technology in the field of medicinal chemistry that seeks to clarify the relationships between molecular structures and their biological activities. Such technologies allow for the acceleration of the development of new compounds by reducing the costs of drug design. This work presents 3D-QSARpy, a flexible, user-friendly and robust tool, freely available without registration, to support the generation of QSAR 3D models in an automated way. The user only needs to provide aligned molecular structures and the respective dependent variable. The current version was developed using Python with packages such as scikit-learn and includes various techniques of machine learning for regression. The diverse techniques employed by the tool is a differential compared to known methodologies, such as CoMFA and CoMSIA, because it expands the search space of possible solutions, and in this way increases the chances of obtaining relevant models. Additionally, approaches for select variables (dimension reduction) were implemented in the tool. To evaluate its potentials, experiments were carried out to compare results obtained from the proposed 3D-QSARpy tool with the results from already published works. The results demonstrated that 3D-QSARpy is extremely useful in the field due to its expressive results.

摘要 定量构效关系（Quantitative Structure-Activity Relationship, QSAR）是药物化学领域的计算机辅助技术，旨在阐明分子结构与其生物活性之间的内在关联。此类技术可通过降低药物研发成本，加速新型化合物的开发进程。本研究推出了3D-QSARpy——一款灵活易用、性能稳健的自动化工具，无需注册即可免费获取，用于自动化生成QSAR三维模型。用户仅需提供已对齐的分子结构及对应的因变量即可。该工具的当前版本基于Python结合scikit-learn等第三方库开发，集成了多种回归类机器学习技术。相较于CoMFA与CoMSIA等现有主流方法，本工具所采用的多样化机器学习技术具备显著差异化优势：它拓展了可行解的搜索空间，进而提升了获得有效相关模型的概率。此外，该工具还集成了变量选择（维度约简）相关的算法方案。为评估该工具的应用潜力，本研究开展了对比实验，将3D-QSARpy生成的结果与已发表文献中的相关成果进行比对。实验结果表明，3D-QSARpy凭借优异的建模效果，在该领域具备极高的实用价值。