activity
20232026
most citedReflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

5 citations · 8 across the 13 of their papers we have counts for

collaborators

16 papers

q-bio.QM2026

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…

cs.LG2026

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…

cs.LG2026

Multi-Objective Coverage via Constraint Active Search

Zakaria Shams Siam, Xuefeng Liu, Chong Liu

In this paper, we formulate the new multi-objective coverage (MOC) problem where our goal is to identify a small set of representative samples whose predicted outcomes broadly cove…

cs.LG2025

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…

cs.LG2025

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…

cs.CR2025

WGLE:Backdoor-free and Multi-bit Black-box Watermarking for Graph Neural Networks

Tingzhi Li, Xuefeng Liu, Jing Lei +1

Graph Neural Networks (GNNs) are increasingly deployed in real-world applications, making ownership verification critical to protect their intellectual property against model theft…