3 papers
q-bio.BM2025
Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
Hao Qian, Pu You, Lin Zeng +5
Glioblastoma (GBM) remains the most aggressive tumor, urgently requiring novel therapeutic strategies. Here, we present a dry-to-wet framework combining generative modeling and exp…
cs.LG2025
Full-Atom Peptide Design via Riemannian-Euclidean Bayesian Flow Networks
Hao Qian, Shikui Tu, Lei Xu
Diffusion and flow matching models have recently emerged as promising approaches for peptide binder design. Despite their progress, these models still face two major challenges. Fi…
cs.LG2025
Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number
Jingyuan Zhou, Hao Qian, Shikui Tu +1
Structure-based drug design (SBDD), aiming to generate 3D molecules with high binding affinity toward target proteins, is a vital approach in novel drug discovery. Although recent…