Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A.

“Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A. Qian H(#)(1), You P(#)(2), Zeng L(1), Zhou J(1), Huang D(1), Li K(2), Tu S(1), Xu L(1). Author information: (1)Centre for Cognitive Machines and Computational Health (CMaCH), School of Computer Science, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Minhang District, Shanghai 200240, China. (2)QuietD Bio Co., Ltd., No. 333 Haike Road, Pudong New District, Shanghai 201210, China. (#)Contributed equally Glioblastoma (GBM) remains the most aggressive tumor, urgently requiring novel therapeutic strategies. Here, we present a dry-to-wet framework combining generative modeling and experimental validation to optimize peptides targeting ATP5A, a potential peptide-binding protein for GBM. Our framework introduces the first lead-conditioned generative model, which focuses exploration on geometrically relevant regions around lead peptides and mitigates the combinatorial complexity of de novo methods. Specifically, we propose POTFlow, a Prior and Optimal Transport-based Flow-matching model for peptide optimization. POTFlow employs secondary structure information (e.g. helix, sheet, and loop) as geometric constraints, which are further refined by optimal transport to produce shorter flow paths. With this design, our method achieves state-of-the-art performance compared with five popular approaches. When applied to GBM, our method generates peptides that selectively inhibit cell viability and significantly prolong survival in a patient-derived xenograft model. As the first lead peptide-conditioned flow matching model, POTFlow holds strong potential as a generalizable framework for therapeutic peptide design. © The Author(s) 2026. Published by Oxford University Press.”