14 citations · 32 across the 24 of their papers we have counts for
25 papers
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…
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…
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…
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…
ReactBench: A Benchmark for Topological Reasoning in MLLMs on Chemical Reaction Diagrams
Qiang Xu, Shengyuan Bai, Yu Wang +6
Multimodal Large Language Models (MLLMs) excel at recognizing individual visual elements and reasoning over simple linear diagrams. However, when faced with complex topological str…
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…
Phi-Former: A Pairwise Hierarchical Approach for Compound-Protein Interactions Prediction
Zhe Wang, Zijing Liu, Chencheng Xu +1
Drug discovery remains time-consuming, labor-intensive, and expensive, often requiring years and substantial investment per drug candidate. Predicting compound-protein interactions…
Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis
Hao Li, He Cao, Shenyao Peng +7
Language models are revolutionizing the biochemistry domain, assisting scientists in drug design and chemical synthesis with high efficiency. Yet current approaches struggle betwee…
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…
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…
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…
GPAS: Accelerating Convergence of LLM Pretraining via Gradient-Preserving Activation Scaling
Tianhao Chen, Xin Xu, Zijing Liu +12
Modern Large Language Models, such as the LLaMA, Qwen and DeepSeek series, predominantly adopt the Pre-LayerNorm (Pre-LN) Transformer architecture. While being stable during pretra…
How to Detect and Defeat Molecular Mirage: A Metric-Driven Benchmark for Hallucination in LLM-based Molecular Comprehension
Hao Li, Liuzhenghao Lv, He Cao +6
Large language models are increasingly used in scientific domains, especially for molecular understanding and analysis. However, existing models are affected by hallucination issue…
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…
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…
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…
Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models
Weidi Luo, He Cao, Zijing Liu +5
With the extensive deployment of Large Language Models (LLMs), ensuring their safety has become increasingly critical. However, existing defense methods often struggle with two key…
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.…
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…
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…
Parameter-Efficient Fine-Tuning with Discrete Fourier Transform
Ziqi Gao, Qichao Wang, Aochuan Chen +4
Low-rank adaptation~(LoRA) has recently gained much interest in fine-tuning foundation models. It effectively reduces the number of trainable parameters by incorporating low-rank m…
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…
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…
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…