Computational exploration and discovery of dual EGFR-CDK2 kinase inhibitors: AI-ML powered bioisosteric design, 3D QSAR, docking, DFT and ADMET analysis of novel phthalimide derivatives

AI-ML approaches emerged as transformative technologies in cancer drug discovery by accelerating the target identification and lead optimization. EGFR and CDK2 are crucial targets in cancer therapy which involved in cancer proliferation and metastasis. However, existing inhibitors face challenges like resistance, toxicity and poor pharmacokinetics. Phthalimide scaffolds possess dual or multi-target efficacy which serve a promising drug. This study explores phthalimide-based dual EGFR and CDK2 inhibitors addressing limitations of current anticancer agents. Initially, the 3D-QSAR model was developed and validated using 58 phthalimide derivatives (r2 = 0.998, Q2 = 0.852 and MAE = 0.299). The novel 3886 phthalimide derivatives were generated using the MolOpt server by bioisosteric replacements and screened over the 3D-QSAR model. Notably, 80 novel derivatives demonstrated exceptional anticancer potency (IC50 < 10 nM). Molecular docking, binding free energy, MM-PBSA and MM-GBSA confirmed strong binding affinities, stability and dual action of novel compounds (1472, 1486 and 1458) with EGFR and CDK2. DFT analysis revealed favourable electronic properties and supporting their reactivity. AI-driven ADMET predictions confirmed their drug-like characteristics. This study highlights the AI-ML driven methodologies in the discovery of novel phthalimide derivatives (1472, 1486 and 1458) as potent anticancer agents (IC50 = 3.6, 6.2 and 7.4 nM).