Area of research
Molecular Biology · Computational Theory and Mathematics
Research interest
Research interests include Computational Drug Discovery Methods, DNA and Biological Computing, Bioinformatics and Genomic Networks, and Advanced biosensing and bioanalysis techniques.
ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support
A molecular video-derived foundation model for scientific drug discovery
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction
Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks
Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction
Deep Generative Models in <i>De Novo</i> Drug Molecule Generation
Deep generative molecular design reshapes drug discovery
Deep learning for drug repurposing: Methods, databases, and applications
KG-MTL: Knowledge Graph Enhanced Multi-Task Learning for Molecular Interaction
Effectively Identifying Compound-Protein Interaction Using Graph Neural Representation
ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties
Target identification among known drugs by deep learning from heterogeneous networks
Identifying enhancer–promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism
A novel molecular representation with BiGRU neural networks for learning atom
Drug Target Interaction Prediction using Multi-task Learning and Co-attention
Learning to Predict Drug Target Interaction From Missing Not at Random Labels
Target Identification Among Known Drugs by Deep Learning from Heterogeneous Networks
Drug Target Interaction Prediction with Non-random Missing Labels