Institute of Petroleum Engineering, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
Mol Divers. 2021 Feb;25(1):263-277. doi: 10.1007/s11030-020-10063-9. Epub 2020 Mar 5.
Poly ADP-ribose polymerase-1 (PARP-1) inhibitors have been recognized as new agents for the treatment of patients with breast cancer type 1 (BRCA1) disorders. The quantitative structure-activity relationships (QSAR) technique was used in order to achieve the required medicines for anticancer activity easier and faster. In this study, the QSAR method was developed to predict the half-maximal inhibitory concentration (IC) of 51 1H-benzo[d]immidazole-4-carboxamide derivatives by genetic algorithm-multiple linear regression (GA-MLR) and least squares-support vector machine (LS-SVM) methods. Results in the best QSAR model represented the coefficient of leave-one-out cross-validation (Q ) = 0.971, correlation coefficient (R) = 0.977, Fisher parameter (F) = 259.016 and root mean square error (RMSE) = 0.095, respectively, which indicated that the LS-SVM model had a good potential to predict the pIC (9 - log(IC nM)) values compared with other modeling methods. Also, molecular docking evaluated interactions between ligands and enzyme and their free energy of binding were calculated and used as descriptors. Molecular docking and the QSAR study completed each other. The results represented that the final model can be useful to design some new inhibitors. So, the knowledge of the QSAR modeling and molecular docking was used in pIC prediction and 51 new compounds were developed as PARP-1 inhibitors that 9 compounds had the best-proposed values for pIC. The maximum enhancement of the inhibitory activity of compounds was 33.394%.
聚 ADP-核糖聚合酶-1（PARP-1）抑制剂已被认为是治疗乳腺癌 1 型（BRCA1）疾病患者的新型药物。为了更容易、更快地获得具有抗癌活性的所需药物，采用了定量构效关系（QSAR）技术。在这项研究中，通过遗传算法-多元线性回归（GA-MLR）和最小二乘支持向量机（LS-SVM）方法，开发了 QSAR 方法来预测 51 个 1H-苯并[d]咪唑-4-甲酰胺衍生物的半最大抑制浓度（IC）。最佳 QSAR 模型的结果代表了留一交叉验证系数（Q）= 0.971、相关系数（R）= 0.977、Fisher 参数（F）= 259.016 和均方根误差（RMSE）= 0.095，分别表示与其他建模方法相比，LS-SVM 模型具有良好的预测 pIC（9-log（IC nM））值的潜力。此外，分子对接评估了配体与酶之间的相互作用及其结合自由能，并将其用作描述符。分子对接和 QSAR 研究相互补充。结果表明，最终模型可用于设计一些新的抑制剂。因此，QSAR 建模和分子对接的知识用于 pIC 预测，开发了 51 种新的化合物作为 PARP-1 抑制剂，其中 9 种化合物具有最佳的 pIC 值。化合物的抑制活性最大增强了 33.394%。
