5 papers
ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction
Hexiao Ding, Hongzhao Chen, Jing Lan +12
Accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is important for drug discovery. Most predictors use undirected molecular graphs and pa…
Structure-Aware Contrastive Learning with Fine-Grained Binding Representations for Drug Discovery
Jing Lan, Hexiao Ding, Hongzhao Chen +8
Accurate identification of drug-target interactions (DTI) remains a central challenge in computational pharmacology, where sequence-based methods offer scalability. This work intro…
DeepMoLM: Leveraging Visual and Geometric Structural Information for Molecule-Text Modeling
Jing Lan, Hexiao Ding, Hongzhao Chen +8
AI models for drug discovery and chemical literature mining must interpret molecular images and generate outputs consistent with 3D geometry and stereochemistry. Most molecular lan…
REACT-KD: Region-Aware Cross-modal Topological Knowledge Distillation for Interpretable Medical Image Classification
Hongzhao Chen, Hexiao Ding, Yufeng Jiang +8
Reliable and interpretable tumor classification from clinical imaging remains a core challenge. The main difficulties arise from heterogeneous modality quality, limited annotations…
Contrastive Multi-Task Learning with Solvent-Aware Augmentation for Drug Discovery
Jing Lan, Hexiao Ding, Hongzhao Chen +7
Accurate prediction of protein-ligand interactions is essential for computer-aided drug discovery. However, existing methods often fail to capture solvent-dependent conformational…