Natarajan Ramanathan, Natarajan Ganapathy S, Basak Subhash C
Department of Research and Development, Saranathan College of Engineering, Panjappur, Tiruchirappalli 620 012, Tamil Nadu, India.
Department of Mechanical Engineering and Industrial Engineering, University of Wisconsin-Platteville, Platteville, WI 53818, USA.
Molecules. 2025 Jan 14;30(2):307. doi: 10.3390/molecules30020307.
The development of chirality descriptors for quantitative chirality structure-activity relationship (QCSAR) modeling has always attracted attention, owing to the importance of chiral molecules in pharmaceutical, agriculture, food, and fragrance industries, and environmental toxicology. The utility of a multidimensional space of novel relative chirality indices (RCIs) in the QCSAR modeling of twenty CCR2 antagonists is reported upon in this paper. The numerical characterization of chirality by the RCI approach gives a large pool of chirality descriptors with different degrees of mutual correlation (the correlation coefficient among the computed descriptors varied from 0.02 to 0.99). In the present study, the final data set contains 198 chirality descriptors for each of the twenty CCR2 antagonist molecules, providing a multidimensional space for modeling. The data reduction using principal component analysis resulted in the extraction of eight principal components (PCs). The linear regression using the principal component scores (PCSs) resulted in a three-predictor prediction model with good statistics: R = 0.823; Adj R = 0.790. The regression models were rebuilt using the chirality descriptors (RCIs) that are most correlated with each of the scores (PCSs) of the three principal components. The R value for the regression models with three RCIs as the predictors is 0.742 and the five-fold cross validation, R, is 0.839. The new chirality descriptors, namely, the RCIs calculated using a different weighting scheme, provide a multidimensional space of chirality descriptors for a set of chiral molecules, and such a multidimensional chirality space is a powerful tool to build quantitative chiral structure-activity relationship (QCSAR) models.
由于手性分子在制药、农业、食品、香料工业以及环境毒理学中的重要性，用于定量手性构效关系（QCSAR）建模的手性描述符的开发一直备受关注。本文报道了新型相对手性指数（RCI）的多维空间在20种CCR2拮抗剂的QCSAR建模中的应用。通过RCI方法对手性进行数值表征，得到了大量具有不同相互关联程度的手性描述符（计算得到的描述符之间的相关系数从0.02到0.99不等）。在本研究中，最终数据集包含20种CCR2拮抗剂分子中每种分子的198个手性描述符，为建模提供了一个多维空间。使用主成分分析进行数据降维，提取出了8个主成分（PC）。使用主成分得分（PCS）进行线性回归，得到了一个具有良好统计量的三预测因子预测模型：R = 0.823；调整后R = 0.790。使用与三个主成分的每个得分（PCS）相关性最高的手性描述符（RCI）重建回归模型。以三个RCI作为预测因子的回归模型的R值为0.742，五折交叉验证的R值为0.839。新的手性描述符，即使用不同加权方案计算得到的RCI，为一组手性分子提供了一个手性描述符的多维空间，这样的多维手性空间是构建定量手性构效关系（QCSAR）模型的有力工具。
