Design and evaluation of piperidine carboxamide derivatives as potent ALK inhibitors through 3D-QSAR modeling, artificial neural network and computational analysis

Abstract

Tumor stands as one of the principal contributors to global mortality. As research into tumor treatments advances, tumor inhibitors emerge as pivotal milestones in tumor therapy. Among these inhibitors, Anaplastic Lymphoma Kinase (ALK), a receptor tyrosine kinase, is critical owing to its close association with tumor cell proliferation and growth, which renders it a critical therapeutic target. This work systematically explores the relationship between the chemical structures of 36 piperidine carboxamide derivatives and their efficacy in inhibiting Karpas-299 tumor cell activity by employing a rigorous 3D-QSAR modeling approach. A robust Topomer CoMFA model was generated and was meticulously validated through ANN neural network analysis (q2 = 0.597, r2 = 0.939, F = 84.401, N = 4, SEE = 0.268). Based on the model, 60 new compounds with desirable inhibitory activities were successfully designed. Combined with Lipinski’s rule and ADMET criteria alongside molecular docking and dynamics simulations, a lead compound with high inhibitory activity and good drug-likeness was selected. Further computational analyses, encompassing free energy landscape and binding free energy calculations, provided compelling evidence of the stable binding conformation of the lead compound and the superior affinity with the target protein at the active site, underscoring its potential therapeutic utility. In summary, this investigation offers valuable insights and methodological guidance for advancing tumor therapy and underscores the promise of piperidine carboxamide derivatives as prospective ALK inhibitors.

Keywords

• Tumor
• QSAR
• Artificial neural network
• Piperidine carboxamide derivatives
• ALK inhibitors