AI-driven peptide discovery for endometrial cancer: deep generative modeling and molecular simulation in the big data era.

The integration of artificial intelligence (AI) with molecular modeling offers new opportunities to accelerate therapeutic discovery. In this study, we developed an AI-driven generative pipeline combining deep reinforcement learning (DRL), generative adversarial networks (GANs), and variational autoencoders (VAEs) to design novel peptide-like molecules targeting major proteins implicated in endometrial cancer (EC), including AKT1, ESR1, Connexin-43, and CTNNB1. From over 14,200 generated structures, approximately 2313 peptides met drug-likeness and structural criteria and were screened using deep learning-enhanced docking. Top-ranked peptides, such as Gitoxoside (- 11.53 kcal/mol) and 9-Fluoro-11 (- 11.38 kcal/mol), demonstrated stronger binding to AKT1 than the reference inhibitor Capivasertib (- 8.50 kcal/mol). Similar high-affinity interactions were observed for CTNNB1-SCHEMBL (- 12.33 kcal/mol) and ESR1-1Estra-1,3 (- 11.05 kcal/mol). Molecular dynamics (MD) simulations confirmed the stability of these complexes with RMSD values below 2.5 Å and minimal residue fluctuations. WaterSwap free energy calculations yielded highly favorable binding energies (- 34 to - 37 kcal/mol), further validating stable ligand-protein interactions. ADMET predictions indicated acceptable pharmacokinetic properties and low predicted toxicity for most candidates. Collectively, this integrative AI framework efficiently explores peptide chemical space, enabling the rapid identification of peptide-based and peptidomimetic inhibitors with strong binding affinity and stability. The findings highlight the potential of AI-assisted peptide design as a scalable and cost-effective strategy for developing next-generation therapeutics against endometrial cancer.