2 citations · 5 across the 11 of their papers we have counts for
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…
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…
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…
Bidirectional Hierarchical Protein Multi-Modal Representation Learning
Xuefeng Liu, Songhao Jiang, Chih-chan Tien +2
Protein representation learning is critical for numerous biological tasks. Recently, large transformer-based protein language models (pLMs) pretrained on large scale protein sequen…
ControllableGPT: A Ground-Up Designed Controllable GPT for Molecule Optimization
Xuefeng Liu, Songhao Jiang, Bo Li +1
Large Language Models (LLMs) employ three popular training approaches: Masked Language Models (MLM), Causal Language Models (CLM), and Sequence-to-Sequence Models (seq2seq). Howeve…
DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization
Xuefeng Liu, Songhao Jiang, Siyu Chen +4
Finetuning a Large Language Model (LLM) is crucial for generating results towards specific objectives. This research delves into the realm of drug optimization and introduce a nove…
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…
ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization
Xuefeng Liu, Songhao Jiang, Ian Foster +2
Drug optimization has become increasingly crucial in light of fast-mutating virus strains and drug-resistant cancer cells. Nevertheless, it remains challenging as it necessitates r…
Entropy-Reinforced Planning with Large Language Models for Drug Discovery
Xuefeng Liu, Chih-chan Tien, Peng Ding +2
The objective of drug discovery is to identify chemical compounds that possess specific pharmaceutical properties toward a binding target. Existing large language models (LLMS) can…
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…