Aouidate Adnane, Ghaleb Adib, Ghamali Mounir, Chtita Samir, Ousaa Abdellah, Choukrad M'barek, Sbai Abdelouahid, Bouachrine Mohammed, Lakhlifi Tahar
MCNSL, School of Sciences, Moulay Ismail University, Meknes, Morocco.
High School of Technology, Moulay Ismail University, Meknes, Morocco.
Chem Cent J. 2018 Mar 22;12(1):32. doi: 10.1186/s13065-018-0401-x.
Quantitative structure-activity relationship (QSAR) was carried out to study a series of aminooxadiazoles as PIM1 inhibitors having pk ranging from 5.59 to 9.62 (k in nM). The present study was performed using Genetic Algorithm method of variable selection (GFA), multiple linear regression analysis (MLR) and non-linear multiple regression analysis (MNLR) to build unambiguous QSAR models of 34 substituted aminooxadiazoles toward PIM1 inhibitory activity based on topological descriptors.
Results showed that the MLR and MNLR predict activity in a satisfactory manner. We concluded that both models provide a high agreement between the predicted and observed values of PIM1 inhibitory activity. Also, they exhibit good stability towards data variations for the validation methods. Furthermore, based on the similarity principle we performed a database screening to identify putative PIM1 candidates inhibitors, and predict their inhibitory activities using the proposed MLR model.
This approach can be easily handled by chemists, to distinguish, which ones among the future designed aminooxadiazoles structures could be lead-like and those that couldn't be, thus, they can be eliminated in the early stages of drug discovery process.
开展定量构效关系（QSAR）研究，以考察一系列氨基恶二唑作为PIM1抑制剂的情况，其pK值范围为5.59至9.62（k以nM计）。本研究采用遗传算法变量选择（GFA）、多元线性回归分析（MLR）和非线性多元回归分析（MNLR），基于拓扑描述符构建34种取代氨基恶二唑对PIM1抑制活性的明确QSAR模型。
结果表明，MLR和MNLR对活性的预测效果良好。我们得出结论，这两个模型在PIM1抑制活性的预测值和观测值之间具有高度一致性。此外，对于验证方法，它们对数据变化表现出良好的稳定性。此外，基于相似性原理，我们进行了数据库筛选，以识别潜在的PIM1候选抑制剂，并使用所提出的MLR模型预测它们的抑制活性。
这种方法化学家可以轻松掌握，以区分未来设计的氨基恶二唑结构中哪些可能具有类先导物性质，哪些不具有，从而在药物发现过程的早期阶段将它们排除。
