Grant: Op. Gr.:CIHR-NSERC / Canada-France Call for Proposals on Artificial Intelligence

Claudins (CLDNs) are key cell surface proteins involved in maintaining barrier integrity, yet their overexpression in gastrointestinal (GI) cancers drives tumor progression and metastasis. Despite their therapeutic potential, their dual role in cancer and complex interactome pose challenges for targeted drug discovery. We hypothesize that integrating generative AI (Gen-AI) with multi-omics data can uncover critical CLDN-driven cancer mechanisms and enable the design of small-molecule inhibitors as an alternative to antibody therapies. Objectives: 1) Building a Multi-Omics Database by integrating transcriptomic, proteomic, and clinical datasets to map CLDN expression in GI cancers. 2) Developing AI Models using generative adversarial networks (GANs) and reinforcement learning to identify therapeutic targets and predict druggable sites. 3) Designing Small-Molecule Inhibitors by employing AI-driven de novo drug generation, optimizing molecules for potency, selectivity, and synthesizability. 4) Validating Compounds through molecular docking, molecular dynamics simulations, and experimental testing in patient-derived 3D organoid models and xenograft mice. Led by AI and drug discovery expert Prof. Khaled Barakat (Canada) and CLDN biology specialist Prof. Thomas Baumert (France), the project bridges expertise in computational modeling and experimental validation. Expected outcomes include novel AI methodologies, a public CLDN database, and first-in-class CLDN inhibitors with oral bioavailability, offering an innovative alternative to costly antibody therapies.