Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P. R. China.
Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbaa374.
Matched molecular pairs analysis (MMPA) has become a powerful tool for automatically and systematically identifying medicinal chemistry transformations from compound/property datasets. However, accurate determination of matched molecular pair (MMP) transformations largely depend on the size and quality of existing experimental data. Lack of high-quality experimental data heavily hampers the extraction of more effective medicinal chemistry knowledge. Here, we developed a new strategy called quantitative structure-activity relationship (QSAR)-assisted-MMPA to expand the number of chemical transformations and took the logD7.4 property endpoint as an example to demonstrate the reliability of the new method. A reliable logD7.4 consensus prediction model was firstly established, and its applicability domain was strictly assessed. By applying the reliable logD7.4 prediction model to screen two chemical databases, we obtained more high-quality logD7.4 data by defining a strict applicability domain threshold. Then, MMPA was performed on the predicted data and experimental data to derive more chemical rules. To validate the reliability of the chemical rules, we compared the magnitude and directionality of the property changes of the predicted rules with those of the measured rules. Then, we compared the novel chemical rules generated by our proposed approach with the published chemical rules, and found that the magnitude and directionality of the property changes were consistent, indicating that the proposed QSAR-assisted-MMPA approach has the potential to enrich the collection of rule types or even identify completely novel rules. Finally, we found that the number of the MMP rules derived from the experimental data could be amplified by the predicted data, which is helpful for us to analyze the medicinal chemical rules in local chemical environment. In summary, the proposed QSAR-assisted-MMPA approach could be regarded as a very promising strategy to expand the chemical transformation space for lead optimization, especially when no enough experimental data can support MMPA.
匹配分子对分析（MMPA）已成为一种强大的工具，可用于自动和系统地从化合物/性质数据集中识别药物化学转化。然而，准确确定匹配分子对（MMP）转化在很大程度上取决于现有实验数据的大小和质量。缺乏高质量的实验数据严重阻碍了更有效的药物化学知识的提取。在这里，我们开发了一种称为定量构效关系（QSAR）辅助-MMPA 的新策略来扩展化学转化的数量，并以 logD7.4 性质终点为例来证明新方法的可靠性。首先建立了可靠的 logD7.4 共识预测模型，并严格评估了其适用域。通过将可靠的 logD7.4 预测模型应用于筛选两个化学数据库，我们通过定义严格的适用性域阈值获得了更多高质量的 logD7.4 数据。然后，对预测数据和实验数据进行 MMPA，以推导出更多的化学规则。为了验证化学规则的可靠性，我们比较了预测规则和实测规则的性质变化的幅度和方向性。然后，我们将我们提出的方法生成的新颖化学规则与已发表的化学规则进行了比较，发现性质变化的幅度和方向性是一致的，这表明所提出的 QSAR 辅助-MMPA 方法有可能丰富规则类型的集合，甚至可以识别全新的规则。最后，我们发现预测数据可以放大实验数据中推导的 MMP 规则的数量，这有助于我们在局部化学环境中分析药物化学规则。总之，所提出的 QSAR 辅助-MMPA 方法可以被视为一种非常有前途的策略，可以扩展用于先导优化的化学转化空间，特别是当没有足够的实验数据支持 MMPA 时。
