11 papers
Leveraging generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design
Xuefeng Liu, Mingxuan Cao, Xiao Luo +5
Biomolecular design underpins applications from molecular recognition to therapeutics and synthetic biology, yet de novo interaction design remains challenging-especially for DNA/R…
Active-GRPO: Adaptive Imitation and Self-Improving Reasoning for Molecular Optimization
Xuefeng Liu, Mingxuan Cao, Qinan Huang +3
Scientific reasoning is an increasingly important capability of large language models, yet improving the robustness and efficiency of training such reasoning remains a key open cha…
Active Advantage-Aligned Online Reinforcement Learning with Offline Data
Xuefeng Liu, Hung T. C. Le, Siyu Chen +4
Online reinforcement learning (RL) enhances policies through direct interactions with the environment, but faces challenges related to sample efficiency. In contrast, offline RL le…
Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
Xuefeng Liu, Mingxuan Cao, Songhao Jiang +6
The goal of protein design is to generate amino acid sequences that fold into functional structures with desired properties. Prior methods combining autoregressive language models…
FragmentGPT: A Unified GPT Model for Fragment Growing, Linking, and Merging in Molecular Design
Xuefeng Liu, Songhao Jiang, Qinan Huang +5
Fragment-Based Drug Discovery (FBDD) is a popular approach in early drug development, but designing effective linkers to combine disconnected molecular fragments into chemically an…
Blending Imitation and Reinforcement Learning for Robust Policy Improvement
Xuefeng Liu, Takuma Yoneda, Rick L. Stevens +2
While reinforcement learning (RL) has shown promising performance, its sample complexity continues to be a substantial hurdle, restricting its broader application across a variety…