3 papers
q-bio.BM2026
RNA Design via Conditioned Flow Matching and Finite-Policy Reinforcement Learning
Zefeng Lin, Xianyong Fang, Tianfan Fu +1
RNA design aims to identify sequences that fold into specified secondary structures. Existing methods formulate the task as target-specific search or conditional generation. Howeve…
q-bio.BM2026
EnzyPGM: Pocket-conditioned Generative Model for Substrate-specific Enzyme Design
Zefeng Lin, Zhihang Zhang, Weirong Zhu +4
Designing enzymes with substrate-binding pockets is a critical challenge in protein engineering, as catalytic activity depends on the precise interaction between pockets and substr…
cs.CV2024
TrojFlow: Flow Models are Natural Targets for Trojan Attacks
Zhengyang Qi, Xiaohua Xu
Flow-based generative models (FMs) have rapidly advanced as a method for mapping noise to data, its efficient training and sampling process makes it widely applicable in various fi…