Abstract

The discovery of selective immunosuppressants for T cell-mediated diseases like Ulcerative Colitis (UC) is a significant challenge. While traditional screening is inefficient, Artificial intelligence (AI)-driven approaches are often hindered by the scarcity of high-quality labeled data, challenging the accurate identification of functional molecules. In this study, we leveraged a transfer learning strategy to compensate for the lack of high-quality data, establishing a screening platform to identify potential T cell inhibitors. Using this approach, we identified Lutein as a novel, specific immunomodulatory candidate from a natural product library. Integrated multi-omics analyses revealed that Lutein activates peroxisome proliferator-activated receptor gamma (PPARγ), suppressing glucose uptake and glycolysis, thereby selectively inhibiting Th1 cell differentiation. In a dextran sulfate sodium (DSS)-induced mouse model of ulcerative colitis, Lutein treatment significantly restored Th1-mediated immune balance and alleviated pathological tissue damage. Our findings highlight the feasibility of using a few-shot learning strategy based on transfer learning to screen for specific immunosuppressants and indicate that Lutein is a promising therapeutic candidate for ulcerative colitis. The alternative text for this image may have been generated using AI. An AI screening platform leveraging the Uni-Mol model identifies Lutein as a selective Th1 immunomodulator, which targets PPARγ-mediated glucose metabolism to inhibit Th1 differentiation and ameliorate colitis.