<h2 style="text-align: left; color: #1a73e8; border-bottom: 2px solid #1a73e8; padding-bottom: 8px;"> RESEARCH </h2>

H2CL-DTI: Hierarchical Hypergraph Contrastive Learning for Robust Drug-Target Interaction Prediction

My research work focuses on the application of artificial intelligence and machine learning techniques to real-world problems. This research explores a hierarchical hypergraph contrastive learning approach for robust drug-target interaction prediction.

The research paper, "H2CL-DTI: Hierarchical Hypergraph Contrastive Learning for Robust Drug-Target Interaction Prediction" , was accepted at the IEEE QPAIN 2026 conference with minor revision.

The work was also presented at the IEEE QPAIN 2026 International Conference on Quantum Photonics, Artificial Intelligence and Networking .

Research Area: Artificial Intelligence, Machine Learning, Deep Learning, and Drug-Target Interaction Prediction

Conference: IEEE QPAIN 2026