In the process of drug discovery, the optimization of lead compounds has always been a challenge faced by pharmaceutical chemists. Matched molecular pair analysis (MMPA), a promising tool to efficiently extract and summarize the relationship between structural transformation and property change, is suitable for local structural optimization tasks. Especially, the integration of MMPA with QSAR modeling can further strengthen the utility of MMPA in molecular optimization navigation. In this study, a new semi-automated procedure based on KNIME was developed to support MMPA on both large- and small-scale datasets, including molecular preparation, QSAR model construction, applicability domain evaluation, and MMP calculation and application. Two examples covering regression and classification tasks were provided to gain a better understanding of the importance of MMPA, which has also shown the reliability and utility of this MMPA-by-QSAR pipeline.

中文翻译：

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用于分子对分析和 QSAR 辅助变换空间扩展的半自动工作流程

在药物发现过程中，先导化合物的优化一直是药物化学家面临的挑战。匹配分子对分析 (MMPA) 是一种有效提取和总结结构转变与性质变化之间关系的有前途的工具，适用于局部结构优化任务。特别是，MMPA 与 QSAR 建模的集成可以进一步加强 MMPA 在分子优化导航中的实用性。在这项研究中，开发了一种基于 KNIME 的新的半自动化程序，以支持大型和小型数据集上的 MMPA，包括分子制备、QSAR 模型构建、适用性域评估以及 MMP 计算和应用。