7 papers
Interleaved Tool-Call Reasoning for Protein Function Understanding
Chuanliu Fan, Zicheng Ma, Huanran Meng +6
Recent advances in large language models (LLMs) have highlighted the effectiveness of chain-of-thought reasoning in symbolic domains such as mathematics and programming. However, o…
ProtTeX-CC: Activating In-Context Learning in Protein LLM via Two-Stage Instruction Compression
Chuanliu Fan, Zicheng Ma, Jun Gao +5
Recent advances in protein large language models, such as ProtTeX, represent both side-chain amino acids and backbone structure as discrete token sequences of residue length. While…
Large Language Models as AI Agents for Digital Atoms and Molecules: Catalyzing a New Era in Computational Biophysics
Yijie Xia, Xiaohan Lin, Zicheng Ma +9
In computational biophysics, where molecular data is expanding rapidly and system complexity is increasing exponentially, large language models (LLMs) and agent-based systems are f…
Prot2Chat: Protein LLM with Early-Fusion of Text, Sequence and Structure
Zhicong Wang, Zicheng Ma, Ziqiang Cao +3
Motivation: Proteins are of great significance in living organisms. However, understanding their functions encounters numerous challenges, such as insufficient integration of multi…
ElaLoRA: Elastic & Learnable Low-Rank Adaptation for Efficient Model Fine-Tuning
Huandong Chang, Zicheng Ma, Mingyuan Ma +4
Low-Rank Adaptation (LoRA) has become a widely adopted technique for fine-tuning large-scale pre-trained models with minimal parameter updates. However, existing methods rely on fi…
ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models
Zicheng Ma, Chuanliu Fan, Zhicong Wang +7
Large language models have made remarkable progress in the field of molecular science, particularly in understanding and generating functional small molecules. This success is larg…