195 citations · 202 across the 22 of their papers we have counts for
22 papers
UniField: RBF-Guided Electron Density Fusion for Enhanced Molecular Representations
Wei Zhang, Kun Li, Jiameng Chen +4
Current 3D geometric molecular representations predominantly focus on discrete atomic skeletons, inherently overlooking the continuous electron density (ED) field that fundamentall…
When Molecular Similarity Works: Property Cliffs Reveal Hidden Errors
Di Hu, Kun Li, Haojie Rao +6
Accurate prediction of molecular properties underpins drug discovery and material design, yet even state-of-the-art models remain vulnerable to localized failure modes that aggrega…
Learning to Select Source Domains: Proxy-Rewarded Policy Optimization for Molecular OOD Generalization
Zhuohao Lin, Kun Li, Jiameng Chen +4
Molecular property prediction under severe out-of-distribution (OOD) shifts remains challenging because conventional scaffold splits can retain local structural similarity between…
From Single-Step Edit Response to Multi-Step Molecular Optimization
Haojie Rao, Kun Li, Yida Xiong +5
Conditional molecular optimization aims to edit a molecule to realize a specified property shift. In practice, structurally similar molecule data is scarce, while decisions are inh…
Variational Bayesian Flow Network for Graph Generation
Yida Xiong, Jiameng Chen, Xiuwen Gong +3
Graph generation aims to sample discrete node and edge attributes while satisfying coupled structural constraints. Diffusion models for graphs often adopt largely factorized forwar…
PCEvo: Path-Consistent Molecular Representation via Virtual Evolutionary
Kun Li, Longtao Hu, Yida Xiong +6
Molecular representation learning aims to learn vector embeddings that capture molecular structure and geometry, thereby enabling property prediction and downstream scientific appl…
FP-AbDiff: Improving Score-based Antibody Design by Capturing Nonequilibrium Dynamics through the Underlying Fokker-Planck Equation
Jiameng Chen, Yida Xiong, Kun Li +4
Computational antibody design holds immense promise for therapeutic discovery, yet existing generative models are fundamentally limited by two core challenges: (i) a lack of dynami…
Transport-Coupled Bayesian Flows for Molecular Graph Generation
Yida Xiong, Jiameng Chen, Kun Li +5
Molecular graph generation (MGG) is essentially a multi-class generative task, aimed at predicting categories of atoms and bonds under strict chemical and structural constraints. H…
BSL: A Unified and Generalizable Multitask Learning Platform for Virtual Drug Discovery from Design to Synthesis
Kun Li, Zhennan Wu, Yida Xiong +8
Drug discovery is of great social significance in safeguarding human health, prolonging life, and addressing the challenges of major diseases. In recent years, artificial intellige…
Zero-Shot Learning with Subsequence Reordering Pretraining for Compound-Protein Interaction
Hongzhi Zhang, Zhonglie Liu, Kun Meng +6
Given the vastness of chemical space and the ongoing emergence of previously uncharacterized proteins, zero-shot compound-protein interaction (CPI) prediction better reflects the p…
Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding
Jiameng Chen, Xiantao Cai, Jia Wu +1
Antibody design remains a critical challenge in therapeutic and diagnostic development, particularly for complex antigens with diverse binding interfaces. Current computational met…
DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery
Kun Li, Zhennan Wu, Shoupeng Wang +3
Large language models (LLMs) integrated with autonomous agents hold significant potential for advancing scientific discovery through automated reasoning and task execution. However…
Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities
Kun Li, Yida Xiong, Hongzhi Zhang +4
Due to their excellent drug-like and pharmacokinetic properties, small molecule drugs are widely used to treat various diseases, making them a critical component of drug discovery.…
Can Molecular Evolution Mechanism Enhance Molecular Representation?
Kun Li, Longtao Hu, Xiantao Cai +2
Molecular evolution is the process of simulating the natural evolution of molecules in chemical space to explore potential molecular structures and properties. The relationships be…
Text-guided multi-property molecular optimization with a diffusion language model
Yida Xiong, Kun Li, Jiameng Chen +4
Molecular optimization (MO) is a crucial stage in drug discovery in which task-oriented generated molecules are optimized to meet practical industrial requirements. Existing mainst…
Fragment-Masked Diffusion for Molecular Optimization
Kun Li, Xiantao Cai, Jia Wu +4
Molecular optimization is a crucial aspect of drug discovery, aimed at refining molecular structures to enhance drug efficacy and minimize side effects, ultimately accelerating the…
A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction
Hongzhi Zhang, Xiuwen Gong, Shirui Pan +3
Drug-target interaction (DTI) prediction is a critical component of the drug discovery process. In the drug development engineering field, predicting novel drug-target interactions…
Regressor-free Molecule Generation to Support Drug Response Prediction
Kun Li, Xiuwen Gong, Shirui Pan +3
Drug response prediction (DRP) is a crucial phase in drug discovery, and the most important metric for its evaluation is the IC50 score. DRP results are heavily dependent on the qu…
CLDR: Contrastive Learning Drug Response Models from Natural Language Supervision
Kun Li, Wenbin Hu
Deep learning-based drug response prediction (DRP) methods can accelerate the drug discovery process and reduce R\&D costs. Although the mainstream methods achieve high accuracy in…
Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening
Kun Li, Yong Luo, Xiantao Cai +2
Conventional deep learning methods typically employ supervised learning for drug response prediction (DRP). This entails dependence on labeled response data from drugs for model tr…
Towards a Better Model with Dual Transformer for Drug Response Prediction
Kun Li, Jia Wu, Bo Du +4
GNN-based methods have achieved excellent results as a mainstream task in drug response prediction tasks in recent years. Traditional GNN methods use only the atoms in a drug molec…
Deep Learning for Community Detection: Progress, Challenges and Opportunities
Fanzhen Liu, Shan Xue, Jia Wu +6
As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and d…