Author

Pablo Lemos

SandboxAQORCID 0000-0002-4728-8473Unclaimed 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 Approach:graph neural network· 1 Assay:protein structure· 1

Frequent Co-Authors

Jordan E. Crivelli-DeckeriD· 2 shared Maarten Van Damme· 2 shared Martin Ganahl· 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

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2 shared papersMolecular Biology

Jordan E. Crivelli-DeckerORCID

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2 papers

Recent·Most cited

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

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