Author

Mary Pitman

SandboxAQORCID 0000-0002-2613-5769Unclaimed 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 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Assay:protein sequence· 1 Assay:protein structure· 1

Frequent Co-Authors

Amogh SoodiD· 2 shared Andrea BortolatoiD· 2 shared Kevin RyczkoiD· 2 shared Benjamin Shields· 1 shared Da TengiD· 1 shared Dominic A. RufaiD· 1 shared Harish Ramadas· 1 shared Jordan E. Crivelli-DeckeriD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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2 shared papersBiophysics

Amogh SoodORCID

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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

𝕏 PostBluesky

Molecular BiologybioRxivPreprintMar 4, 2026

On improving experimental binding affinity predictions with synthetic data

Kevin Ryczko, Phyo Phyo Kyaw Zin, Jordan E. Crivelli-Decker…·Ly Le

The paper improves binding affinity predictions by using synthetic data, showing that combining synthetic and experimental data enhances model performance.

DOI 10.64898/2026.03.02.708607

statistical modeldeep learning

𝕏 PostBluesky