Almukadi Haifa, Jadkarim Gada Ali, Mohammed Arif, Almansouri Majid, Sultana Nasreen, Shaik Noor Ahmad, Banaganapalli Babajan
Department of Pharmacology and Toxicology, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia.
Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.
Front Chem. 2023 Mar 10;11:1137444. doi: 10.3389/fchem.2023.1137444. eCollection 2023.
PIM kinases are targets for therapeutic intervention since they are associated with a number of malignancies by boosting cell survival and proliferation. Over the past years, the rate of new PIM inhibitors discovery has increased significantly, however, new generation of potent molecules with the right pharmacologic profiles were in demand that can probably lead to the development of Pim kinase inhibitors that are effective against human cancer. In the current study, a machine learning and structure based approaches were used to generate novel and effective chemical therapeutics for PIM-1 kinase. Four different machine learning methods, namely, support vector machine, random forest, k-nearest neighbour and XGBoost have been used for the development of models. Total, 54 Descriptors have been selected using the Boruta method. SVM, Random Forest and XGBoost shows better performance as compared to k-NN. An ensemble approach was implemented and, finally, four potential molecules (CHEMBL303779, CHEMBL690270, MHC07198, and CHEMBL748285) were found to be effective for the modulation of PIM-1 activity. Molecular docking and molecular dynamic simulation corroborated the potentiality of the selected molecules. The molecular dynamics (MD) simulation study indicated the stability between protein and ligands. Our findings suggest that the selected models are robust and can be potentially useful for facilitating the discovery against PIM kinase.
PIM激酶是治疗干预的靶点，因为它们通过促进细胞存活和增殖与多种恶性肿瘤相关。在过去几年中，新型PIM抑制剂的发现率显著提高，然而，仍需要新一代具有合适药理学特征的强效分子，这可能会导致开发出对人类癌症有效的Pim激酶抑制剂。在当前的研究中，采用了基于机器学习和结构的方法来生成针对PIM-1激酶的新型有效化学治疗剂。四种不同的机器学习方法，即支持向量机、随机森林、k近邻和XGBoost，已被用于模型的开发。使用Boruta方法总共选择了54个描述符。与k-NN相比，支持向量机、随机森林和XGBoost表现出更好的性能。实施了一种集成方法，最终发现四种潜在分子（CHEMBL303779、CHEMBL690270、MHC07198和CHEMBL748285）对调节PIM-1活性有效。分子对接和分子动力学模拟证实了所选分子的潜力。分子动力学（MD）模拟研究表明蛋白质和配体之间的稳定性。我们的研究结果表明，所选模型具有稳健性，可能有助于发现针对PIM激酶的药物。
