Virendra Singh Kaira

This paper develops a machine learning framework using sequence descriptors to predict protein-protein interactions, achieving high accuracy with Random Forest and XGBoost models.

HMVA-DDI predicts drug-drug interactions and their severity using a two-stage deep learning framework with improved attention mechanisms and task-specific features.

This paper introduces a graph transformer model for drug-target binding affinity prediction, showing improved performance on benchmark datasets.

XAttn-DTA predicts drug-target affinity using graph attention networks and cross-attention fusion, achieving improved performance on benchmark datasets.