Author

Parth Bibekar

École Polytechnique Fédérale de LausanneORCID 0000-0002-7767-4447Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Organism:human· 1 Assay:proteomics· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Aaron L. FelleriD· 1 shared Abel GurungiD· 1 shared Ajasja LjubetičiD· 1 shared Akshay ChennaiD· 1 shared Alberto Perez· 1 shared Alejandro Diaz· 1 shared Alex M. AbeliD· 1 shared Alia Clark‐ElSayediD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

Aaron L. FellerORCID

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2 papers

Recent·Most cited

BioinformaticsMolecular Systems BiologyPeer-reviewedOct 8, 2026

Validation and analysis of 12,000 AI-driven CAR-T designs in the Bits to Binders competition

Clayton W. Kosonocky, Alex M. Abel, Aaron L. Feller…·Rohit Satija

The paper validates and analyzes 12,000 AI-designed CAR-T binders from a global competition, identifying successful designs and factors improving their performance.

10 citationstop 1%

humanproteomics

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BioinformaticsbioRxivPreprintJun 21, 2025

Context-aware geometric deep learning for RNA sequence design

Parth Bibekar, Lucien F. Krapp, Matteo Dal Peraro

This paper introduces RISoTTo, a geometric deep learning method for RNA sequence design that incorporates molecular context, showing improved accuracy and binding affinity in in silico tests.

DOI 10.1101/2025.06.21.660801

deep learningsequence-to-function modeling

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