Author

Sean McCarty

University of Michigan

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

Biology Disciplines

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Biophysics· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Research Focus:protein structure prediction· 2 Assay:proteomics· 2 Organism:virus· 2 Research Focus:drug response / drug discovery· 1 Assay:protein structure· 1 Approach:statistical model· 1

Frequent Co-Authors

Terra SztainiD· 3 shared Daniel Santos Perez· 2 shared Shu-Hang LiniD· 2 shared Aleksander E. P. DurumericiD· 1 shared Grace ArhiniD· 1 shared Jay Smith· 1 shared Jonas Köhler· 1 shared Katarina EleziD· 1 shared

Activity by Year

2026-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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3 shared papersBiophysics

Terra SztainORCID

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Author profile →

3 papers

Recent·Most cited

Mechanism of molecular recognition revealed through dynamic drug binding pathways to SARS-CoV-2 main protease

Daniel Santos Perez, Grace Arhin, Yao Fu, Sean McCarty, Shu-Hang Lin, Terra Sztain

This study reveals the molecular mechanism of nirmatrelvir binding to SARS-CoV-2 main protease using combined NMR titrations and weighted ensemble simulations, identifying two distinct binding pathways and key residues involved in resistance.

bioRxiv · Sep 28, 2026 · PreprintBiophysics

Assessing the Generalizability of Machine Learning and Physics-Based Methods with DNA-Encoded Libraries

Marissa Dolorfino, Daniel Santos Perez, Yao Fu, Shu-Hang Lin, Sean McCarty, Matthew J. O’Meara, Terra Sztain

The paper evaluates ML and physics-based methods for predicting protein-ligand binding using DNA-encoded libraries, finding that no single method consistently generalizes to out-of-distribution chemical space.

Journal of Chemical Information and Modeling · Sep 18, 2026Peer-reviewedBiophysics

Machine Learning Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments

Aleksander E. P. Durumeric, Sean McCarty, Jay Smith, Jonas Köhler, Katarina Elez, Lluı́s Raich, Patricia Suriana, Terra Sztain

The paper trains machine learning models on deep mutational scanning data to predict SARS-CoV-2 RBD variant fitness and outperforms point mutation averaging in extrapolating to Omicron variants.

Journal of Chemical Information and Modeling · May 6, 2026Peer-reviewedBioinformatics