Author

Sai Shankar P

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 3 Approach:graph neural network· 2 Approach:deep learning· 1 Assay:protein structure· 1 Assay:proteomics· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Jaiganesh GiD· 3 shared Ruchika BhatiD· 3 shared Virendra Singh KairaiD· 3 shared Zakeerhussain D KudariiD· 3 shared Bibekananda HatiiD· 1 shared

Activity by Year

2026-2026 · peak 3/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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3 shared papersBioinformatics

Jaiganesh GORCID

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3 papers

Recent·Most cited

HMVA-DDI: A Hierarchical Multi-View Attention Network for Drug-Drug Interaction and Severity Prediction

Bibekananda Hati, Ruchika Bhat, Zakeerhussain D Kudari, Virendra Singh Kaira, Sai Shankar P, Jaiganesh G

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

bioRxiv · Oct 1, 2026 · PreprintBioinformatics

Structure-aware graph attention based hierarchical transformer framework for drug-target binding affinity prediction

Virendra Singh Kaira, Zakeerhussain D Kudari, Sai Shankar P, Ruchika Bhat, Jaiganesh G

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

bioRxiv · Apr 22, 2026 · PreprintBioinformatics

Sequence-Driven Drug-Target Affinity Prediction Via Graph Attention Networks and Bidirectional Cross-Attention Fusion

Zakeerhussain D Kudari, Virendra Singh Kaira, Sai Shankar P, Ruchika Bhat, Jaiganesh G

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

bioRxiv · Apr 6, 2026 · PreprintBioinformatics