Yang Zi-Yi, Fu Li, Lu Ai-Ping, Liu Shao, Hou Ting-Jun, Cao Dong-Sheng
Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China.
Hunan Key Laboratory of Diagnostic and Therapeutic Drug Research for Chronic Diseases, Changsha, 410013, Hunan, China.
J Cheminform. 2021 Nov 13;13(1):86. doi: 10.1186/s13321-021-00564-6.
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.
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化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与定量构效关系（QSAR）建模的整合可以进一步增强MMPA在分子优化导航中的效用。在本研究中，开发了一种基于KNIME的新的半自动程序，以支持在大规模和小规模数据集上进行MMPA，包括分子制备、QSAR模型构建、适用域评估以及MMP计算和应用。提供了两个涵盖回归和分类任务的例子，以更好地理解MMPA的重要性，这也展示了这种基于QSAR的MMPA流程的可靠性和实用性。
