Author

Marissa Dolorfino

University of MichiganORCID 0000-0003-2759-7452Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Research Focus:protein design· 1 Assay:protein sequence· 1 Assay:proteomics· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Anastassia Andreevna VorobievaiD· 1 shared Daniel Santos Perez· 1 shared Matthew J. O’MearaiD· 1 shared Rituparna SamantaiD· 1 shared Sean McCarty· 1 shared Shu-Hang LiniD· 1 shared Terra SztainiD· 1 shared Yao FuiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 16 mutual co-author links

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1 shared paperBioinformatics

Anastassia Andreevna VorobievaORCID

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2 papers

Recent·Most cited

BiophysicsJournal of Chemical Information and ModelingPeer-reviewedSep 18, 2026

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

Marissa Dolorfino, Daniel Santos Perez, Yao Fu…·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.

1 citation

proteomicsmachine learning (classical)

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BioinformaticsbioRxivPreprintJan 16, 2024

ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-Barrels

Marissa Dolorfino, Rituparna Samanta, Anastassia Andreevna Vorobieva

This study benchmarks ProteinMPNN for designing transmembrane β-barrel proteins, showing it outperforms Franklin2023 with refined inputs and generates sequences suitable for experimental validation.

5 citations

protein sequencedeep learning

𝕏 PostBluesky