Chen, L.; Leung, K.; Long, Y.; Xu, Z.; Zhang, N.; Chen, G.; Chen, W.; Chen, Z.; Wang, A.; Liang, Z.; Wang, Y.; Zeng, Y.

The glucose-dependent insulinotropic polypeptide receptor (GIPR) is an attractive therapeutic target for metabolic disorders, with GIPR antagonism emerging as a promising strategy for obesity and type 2 diabetes. However, developing functional antibodies against GPCRs remains challenging due to their complex architecture and conformational dynamics. Here, we employed AlfaBodY, an iterative active learning platform integrating structural and sequence information, to in silico design human anti-GIPR antibodies. Through four rounds of optimization, we generated antibodies with high binding affinities . Lead candidates AB106 131 (KD 1.2nM) and AB106 156 (KD 1.7nM) exhibited 7 to 10 fold higher affinity than 2G10 (KD 12 nM) while maintaining comparable antagonistic activity in a cAMP reporter assay (IC504~5nM). In diet induced obese mice, AB106-156 alone induced weight loss comparable to that of semaglutide (~ -15%), while preserving lean mass and achieving sustained weight control after treatment withdrawal. Co administration with the GLP 1 receptor agonist semaglutide produced synergistic weight reduction (-25.4%) and markedly attenuated the fat mass rebound observed with semaglutide alone. Our results demonstrate that AI driven design can generate potent anti GIPR antibodies with favourable in vivo efficacy, supporting further development of GIPR antagonist for obesity and related metabolic disorders. The AlfaBodY platform enables faster development of more efficacious biologic drugs.