3 citations · 6 across the 18 of their papers we have counts for
18 papers
Architecture-Aware Explanation Auditing for Industrial Visual Inspection
Sibo Jia, Zihang Zhao, Kunrong Li
Industrial visual inspection systems increasingly rely on deep classifiers whose heatmap explanations may appear visually plausible while failing to identify the image regions that…
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…
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…
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…
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…