Abstract AI CAR Loop 1.0 is a modular, AI-driven platform designed to accelerate Chimeric Antigen Receptor T-cell (CAR-T) therapy development for solid and hematological malignancies, with all demonstrations conducted through computational validation. The platform integrates five interoperable modules (M1–M5): multi-modal antigen discovery, AlphaFold2-based structural modeling and HADDOCK docking of antigen–scFv complexes, mRNA delivery optimization, in vivo feedback simulation, and reinforcement learning optimization. Leveraging Coscientist’s multi-agent framework (51-day optimization, 40% yield increase 3), it enables rapid, cost-effective prototyping and supports integration of strategic modules like DrugDomain 2.0, AuroBind, REAP, and SMART. Illustrated with CLDN18.2-positive gastric cancer, in silico analyses with public datasets (TCGA, n=375; TCIA, n=200) demonstrate a 40–50% cost reduction, 80–85% target accuracy (±5%, vs. 60–70% traditional methods), and 18–24-month development cycles. All results are derived from computational simulations, with no wet-lab or clinical testing performed. Cross-validation and uncertainty quantification ensure robust metrics. Future work will pursue in vitro and in vivo validation to translate these computational insights into clinical applications.
X. Hu (Wed,) studied this question.
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