Author

Conor T. McMahon

Harvard UniversityORCID 0000-0001-9971-7869Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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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:variant effect prediction· 1

Frequent Co-Authors

Aaron W. KollaschiD· 2 shared Adam J. RiesselmaniD· 2 shared Andrew C. KruseiD· 2 shared Debora S. MarksiD· 2 shared Aashish ManglikiD· 1 shared Andre A. R. TeixeiraiD· 1 shared Artem GazizoviD· 1 shared Chris SanderiD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersBioinformatics

Aaron W. KollaschORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJan 12, 2026

Machine Learning enables efficient and effective affinity maturation of nanobodies

Steffan B. Paul, Edward P. Harvey, James Osei‐Owusu…·Andrew C. Kruse

This paper uses machine learning to predict high-affinity nanobody binders from sparse sequencing data, outperforming deep learning and semi-supervised methods with linear models.

2 citations

proteomicsmachine learning (classical)

𝕏 PostBluesky

Systems BiologyNature CommunicationsPeer-reviewedApr 23, 2021

Protein Design and Variant Prediction Using Autoregressive Generative Models

Jung-Eun Shin, Adam J. Riesselman, Aaron W. Kollasch…·Debora S. Marks

This paper introduces an alignment-free deep generative model for protein design and variant effect prediction, showing improved performance over existing methods in challenging cases.

319 citationstop 1%

protein sequencedeep learning

𝕏 PostBluesky