9 citations · 14 across the 8 of their papers we have counts for
9 papers
Multi-objective fluorescent molecule design with a data-physics dual-driven generative framework
Yanheng Li, Zhichen Pu, Lijiang Yang +2
Designing fluorescent small molecules with tailored optical and physicochemical properties requires navigating vast, underexplored chemical space while satisfying multiple objectiv…
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…
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…
ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model
Chuanliu Fan, Ziqiang Cao, Zicheng Ma +5
Goal-oriented de novo molecule design, namely generating molecules with specific property or substructure constraints, is a crucial yet challenging task in drug discovery. Existing…
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…
Invertible Coarse Graining with Physics-Informed Generative Artificial Intelligence
Jun Zhang, Xiaohan Lin, Weinan E +1
Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales. Two specific challenges are commonly present in mu…
PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction
Sirui Liu, Jun Zhang, Haotian Chu +16
Proteins are essential component of human life and their structures are important for function and mechanism analysis. Recent work has shown the potential of AI-driven methods for…
Molecular CT: Unifying Geometry and Representation Learning for Molecules at Different Scales
Jun Zhang, Yao-Kun Lei, Yaqiang Zhou +2
Deep learning is changing many areas in molecular physics, and it has shown great potential to deliver new solutions to challenging molecular modeling problems. Along with this tre…