Author

Martin Ganahl

SandboxAQ

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

Biology Disciplines

Click to filter author papers

Biochemistry· 1 Molecular Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

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

Frequent Co-Authors

Jordan E. Crivelli-DeckeriD· 2 shared Maarten Van Damme· 2 shared Pablo LemosiD· 2 shared Sasaank BandiiD· 2 shared AJ Nish· 1 shared Amogh SoodiD· 1 shared Andrea BortolatoiD· 1 shared Benjamin J. ShieldsiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersMolecular Biology

Jordan E. Crivelli-DeckerORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

On improving experimental binding affinity predictions with synthetic data

Kevin Ryczko, Phyo Phyo Kyaw Zin, Jordan E. Crivelli-Decker, Punit Jha, Harish Ramadas, Lorenzo Boninsegna, Justin Overhulse, Benjamin Shields, Pablo Lemos, Sasaank Bandi, Maarten Van Damme, Amogh Sood, Lee Huntington, Mary Pitman, Martin Ganahl, , Ly Le

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

bioRxiv · Mar 4, 2026 · PreprintMolecular Biology

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