activity
20232026
most citedInstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery

14 citations · 21 across the 18 of their papers we have counts for

collaborators

18 papers

cs.CL2026

Augmenting Molecular Language Models with Local -gram Memory

Xinni Zhang, Zijing Liu, He Cao +2

Transformer-based language models for SMILES strings suffer from a locality gap: standard character-level tokenization fragments chemically meaningful motifs, forcing models to rep…

cs.CE2026

Pushing Biomolecular Utility-Diversity Frontiers with Supergroup Relative Policy Optimization

Xinwu Ye, He Cao, Hao Li +5

Biomolecular generators are often adapted with reward feedback to improve task-specific utility, but pushing utility alone can concentrate generation on a narrow family of candidat…

cs.AI2026

Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach

Ziqi Gao, Chenyi Zi, Zijing Liu +3

Protein-protein interactions (PPIs) are fundamental to cellular function and disease mechanisms. Current learning-based PPI predictors focus on learning powerful protein representa…

cs.LG2026

ProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Design

Yulin Zhang, He Cao, Zihao Jiang +6

Designing proteins with desired functions or properties represents a core goal in synthetic biology and drug discovery. Recent advances in protein language models (PLMs) have enabl…

cs.AI2026★ 1 cited

Mozi: Governed Autonomy for Drug Discovery LLM Agents

He Cao, Siyu Liu, Fan Zhang +7

Tool-augmented large language model (LLM) agents promise to unify scientific reasoning with computation, yet their deployment in high-stakes domains like drug discovery is bottlene…

cs.AI2025

ChemLabs on ChemO: A Multi-Agent System for Multimodal Reasoning on IChO 2025

Qiang Xu, Shengyuan Bai, Leqing Chen +2

Olympiad-level benchmarks in mathematics and physics are crucial testbeds for advanced AI reasoning, but chemistry, with its unique multimodal symbolic language, has remained an op…

cs.LG2025

From Static Structures to Ensembles: Studying and Harnessing Protein Structure Tokenization

Zijing Liu, Bin Feng, He Cao +1

Protein structure tokenization converts 3D structures into discrete or vectorized representations, enabling the integration of structural and sequence data. Despite many recent wor…

physics.chem-ph2025

BioMD: All-atom Generative Model for Biomolecular Dynamics Simulation

Bin Feng, Jiying Zhang, Xinni Zhang +2

Molecular dynamics (MD) simulations are essential tools in computational chemistry and drug discovery, offering crucial insights into dynamic molecular behavior. However, their uti…

physics.comp-ph2025

A Novel 4-D Dataset Paradigm for Studying Complete Ligand-Protein Dissociation Dynamics

Maodong Li, Jiying Zhang, Zhe Wang +7

The kinetics and dynamics of drug-protein binding and dissociation are crucial to understanding drug absorption and metabolism. Despite advances in artificial intelligence (AI) too…

cs.LG2024

Efficient Antibody Structure Refinement Using Energy-Guided SE(3) Flow Matching

Jiying Zhang, Zijing Liu, Shengyuan Bai +3

Antibodies are proteins produced by the immune system that recognize and bind to specific antigens, and their 3D structures are crucial for understanding their binding mechanism an…

cs.CL2024★ 1 cited

SMILES-Prompting: A Novel Approach to LLM Jailbreak Attacks in Chemical Synthesis

Aidan Wong, He Cao, Zijing Liu +1

The increasing integration of large language models (LLMs) across various fields has heightened concerns about their potential to propagate dangerous information. This paper specif…

cs.LG2024

Parameter-Efficient Fine-Tuning via Circular Convolution

Aochuan Chen, Jiashun Cheng, Zijing Liu +4

Low-Rank Adaptation (LoRA) has gained popularity for fine-tuning large foundation models, leveraging low-rank matrices and to represent weight changes (i.…

cs.LG2024

PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes

He Cao, Yanjun Shao, Zhiyuan Liu +4

Multimodal Large Language Models (MLLMs) have seen growing adoption across various scientific disciplines. These advancements encourage the investigation of molecule-text modeling…

cs.LG2024

SubGDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning

Jiying Zhang, Zijing Liu, Yu Wang +1

Molecular representation learning has shown great success in advancing AI-based drug discovery. The core of many recent works is based on the fact that the 3D geometric structure o…

q-bio.BM2024

ControlMol: Adding Substructure Control To Molecule Diffusion Models

Qi Zhengyang, Liu Zijing, Zhang Jiying +2

Due to the vast design space of molecules, generating molecules conditioned on a specific sub-structure relevant to a particular function or therapeutic target is a crucial task in…

cs.CL2024★ 3 cited

MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension

Xingyu Lu, He Cao, Zijing Liu +5

Large language models are playing an increasingly significant role in molecular research, yet existing models often generate erroneous information, posing challenges to accurate mo…

cs.CE2024★ 2 cited

Protein Multimer Structure Prediction via Prompt Learning

Ziqi Gao, Xiangguo Sun, Zijing Liu +3

Understanding the 3D structures of protein multimers is crucial, as they play a vital role in regulating various cellular processes. It has been empirically confirmed that the mult…

q-bio.BM2023★ 14 cited

InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery

He Cao, Zijing Liu, Xingyu Lu +2

The rapid evolution of artificial intelligence in drug discovery encounters challenges with generalization and extensive training, yet Large Language Models (LLMs) offer promise in…