Generative AI-Driven Discovery and Experimental Validation of a Novel EGFR Inhibitor for the Treatment of Pancreatic Cancer
Anuraj Nayarisseri S, Jyothi Kaparapu, Sajal Suhane, Jadhav Vaishnavi, Yug Shree, Dhruvi Jungi, R.J. Sharma, Akansha Sankhala, Swami Radhika, Umesh Panwar, Murali Aarthy, Maddala Madhavi, Srinivas Bandaru, Pranoti Belapurkar, Hafiz Ahmad, Keun Woo Lee, Francisco Jaime Bezerra Mendonça, Luciana Scotti, Luciana Scotti · Scientific Reports · 2026
Pancreatic cancer remains a highly lethal malignancy with limited targeted therapeutic options, necessitating the development of effective EGFR-directed inhibitors. In this study, an integrated generative artificial intelligence (AI)-driven computational–experimental workflow was developed to identify a novel EGFR inhibitor, using a cannabigerol-derived scaffold as the initial seed. ConfGAN, variational autoencoders, generative adversarial networks, and reinforcement learning were employed to explore a chemical space exceeding 1.7 million compounds, followed by progressive prioritization using drug-likeness, synthetic accessibility, molecular docking, and pharmacokinetic filtering. Selected candidates were further evaluated using 200-ns molecular dynamics simulations, free-energy landscape analysis, dynamical cross-correlation analysis, and binding free-energy calculations, with the lead candidate subsequently synthesized and evaluated using biochemical and cellular assays. This workflow identified EGFPAN_EMBS as a chemically distinct EGFR inhibitor. Relative to cannabigerol, EGFPAN_EMBS exhibited improved docking affinity, structural stability, and binding free energy, supporting favorable ligand–EGFR interactions. These computational findings translated into potent biological activity: EGFPAN_EMBS demonstrated nanomolar EGFR kinase inhibitory activity, favorable binding kinetics, and strong antiproliferative activity across pancreatic cancer cell lines, with greater selectivity toward cancer cells than the reference compounds. EGFPAN_EMBS further suppressed EGFR autophosphorylation and downstream PI3K–AKT–MAPK signaling, induced apoptosis, and promoted G₁-phase cell-cycle arrest. These findings support EGFPAN_EMBS as a chemically novel EGFR inhibitor candidate with promising biochemical and cellular activity against pancreatic cancer, and demonstrate the utility of integrating generative AI with structure-based computational analysis and experimental validation for lead discovery. The compound has been deposited in the NCBI PubChem database under Compound ID 10,972,272 and Substance ID 528,216,732.