Author

Punit K. Jha

SandboxAQORCID 0000-0002-1096-4108Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Assay:protein structure· 2 Approach:statistical model· 2 Approach:graph neural network· 1 Assay:protein sequence· 1

Frequent Co-Authors

AJ Nish· 1 shared Amogh SoodiD· 1 shared Andrea BortolatoiD· 1 shared Benjamin J. ShieldsiD· 1 shared Da TengiD· 1 shared Dominic A. RufaiD· 1 shared Jordan E. Crivelli-DeckeriD· 1 shared Kevin RyczkoiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperBiochemistry

AJ Nish

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2 papers

Recent·Most cited

BiophysicsbioRxivPreprintJun 12, 2026

MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen Kinetics

Da Teng, Mary Pitman, Punit K. Jha…·Pratyush Tiwary

MAHLER predicts antibody-antigen dissociation kinetics using a hybrid machine learning and physics approach, achieving screening-grade accuracy and fast computation.

DOI 10.64898/2026.06.10.731383

protein structureprotein sequence

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BiochemistrybioRxivPreprintJun 17, 2025

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

Pablo Lemos, Zane Beckwith, Sasaank Bandi…·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.

10 citations

protein structuredeep learning

𝕏 PostBluesky