Author

Paul Abruzzo

Nvidia (United Kingdom)

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

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Biochemistry· 1

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Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:graph neural network· 1 Assay:protein structure· 1 Approach:statistical model· 1

Frequent Co-Authors

AJ Nish· 1 shared Benjamin J. ShieldsiD· 1 shared Jordan E. Crivelli-DeckeriD· 1 shared Maarten Van Damme· 1 shared Martin Ganahl· 1 shared Nicola De MitriiD· 1 shared Pablo LemosiD· 1 shared Punit K. JhaiD· 1 shared

Activity by Year

2025-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBiochemistry

AJ Nish

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1 paper

Recent·Most cited

SAIR: Enabling deep learning for protein-ligand lnteractions with a synthetic structural dataset

Pablo Lemos, Zane Beckwith, Sasaank Bandi, Maarten Van Damme, Jordan E. Crivelli-Decker, Benjamin J. Shields, Thomas Merth, Punit K. Jha, Nicola De Mitri, Tiffany J. Callahan, AJ Nish, Paul Abruzzo, Romelia Salomón–Ferrer, Martin Ganahl

The paper introduces SAIR, a synthetic dataset of protein-ligand structures with activity data, to improve deep learning models for binding affinity prediction.

bioRxiv · Jun 17, 2025 · PreprintBiochemistry