Area of research
Artificial Intelligence · Materials Chemistry
Research interest
Research interests include Advanced Graph Neural Networks, Topic Modeling, Computational Drug Discovery Methods, and Crystallization and Solubility Studies.
Multi-to-uni modal knowledge transfer pre-training for molecular representation learning
3A Multi-Classification Division-Aggregation Framework for Fake News Detection
DeepInterAware: Deep Interaction Interface‐Aware Network for Improving Antigen‐Antibody Interaction Prediction from Sequence Data
GNNDRP: Graph Neural Network With Multi-Task Learning for Drug Response Prediction
Optimizing the water-energy-food nexus in dryland winter wheat systems on the loess plateau
Controllable Generation of Pathogen‐Specific Antimicrobial Peptides Through Knowledge‐Aware Prompt Diffusion Model
Revealing Herb-Symptom Associations and Mechanisms of Action in Protein Networks Using Subgraph Matching Learning
Cooperative Scheduling of Heterogeneous Farm Machines for Farmlands with Multi-Entry/Exit
Guest Editorial: The Cutting-Edge Artificial Intelligence Techniques and Their Applications in Drug Discovery
Geometric Heterogeneous Graph Neural Network for Protein-Ligand Binding Affinity Prediction
SZBC-AI4TCM: a comprehensive web-based computing platform for traditional Chinese medicine research and development
Chemical conjugation mitigates immunotoxicity of chemotherapy via reducing receptor-mediated drug leakage from lipid nanoparticles
Subgraph-Aware Graph Kernel Neural Network for Link Prediction in Biological Networks
A Network Enhancement Method to Identify Spurious Drug-Drug Interactions
Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction
HimGNN: a novel hierarchical molecular graph representation learning framework for property prediction
Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks.
A subcomponent-guided deep learning method for interpretable cancer drug response prediction
DRLM: A Robust Drug Representation Learning Method and its Applications
GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction.
MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks.
A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection.
PHIAF: prediction of phage-host interactions with GAN-based data augmentation and sequence-based feature fusion.
A Multimodal Framework for Improving <i>in Silico</i> Drug Repositioning With the Prior Knowledge From Knowledge Graphs
Predicting Coding Potential of RNA Sequences by Solving Local Data Imbalance
SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations.
EPIHC: Improving Enhancer-Promoter Interaction Prediction by Using Hybrid Features and Communicative Learning
Predicting drug-disease associations through layer attention graph convolutional network.
A Fast Linear Neighborhood Similarity-Based Network Link Inference Method to Predict MicroRNA-Disease Associations
META-DDIE: predicting drug–drug interaction events with few-shot learning
ERI: Towards Smarter Roads: Leveraging Cooperative Perception and Generative Models for Next-Generation Intelligent Transportation Systems
CRII: CSR: Empowering Sustainability and Intelligence into Air-ground Collaborative Systems
NSF-BSF: Electrified Membrane System for Chemical-Free Nitrogen Recovery from Nitrate Contaminated Water
Interfacially Engineered Membranes for Simultaneous Microwave Catalysis and Liquid Filtration
PFI-TT: Electrochemically Reactive Membrane Filtration for Enhanced Recalcitrant Pollutant Removal
I-Corps: Reactive Nanobubbles Technology for Green and Sustainable Environmental and Agricultural Applications
Probing Facet Dependent Properties of Crystalline Nanomaterials and Interactions with Biomolecules using Hybrid AFM
An overlooked source of N-nitrosamine precursors: Examining the role of biofilm in chloraminated drinking water distribution systems
SusChEM: Collaborative Research: Development of Multifunctional Reactive Electrochemical Membranes for Biomass Recovery with Fouling Reduction, Water Reuse, and Cell Pretreatment
I-Corps: Multifunctional Ceramic Reactive Electrochemical Membrane Filtration