Author

J. A. Ruffolo

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· 3 Bioengineering· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 4 Assay:protein structure· 3 Research Focus:protein structure prediction· 3 Research Focus:drug response / drug discovery· 2 Organism:human· 1 Research Focus:protein design· 1 Assay:protein sequence· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

J. J. Gray· 4 shared J. Sulam· 2 shared S. P. Mahajan· 2 shared C. Guerra· 1 shared D. Akpinaroglu· 1 shared R. W. Shuai· 1 shared

Activity by Year

2020-2021 · peak 3/yr

Co-Author Collaboration Network

6 top collaborators · 9 mutual co-author links

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4 shared papersBiophysics

J. J. Gray

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Author profile →

4 papers

Recent·Most cited

Generative language modeling for antibody design

R. W. Shuai, J. A. Ruffolo, J. J. Gray

This paper introduces IgLM, a generative language model for designing synthetic antibody sequences with improved developability.

bioRxiv · Dec 13, 2021Bioengineering

Improved antibody structure prediction by deep learning of side chain conformations

D. Akpinaroglu, J. A. Ruffolo, S. P. Mahajan, J. J. Gray

This paper introduces DeepSCAb, a deep learning method that improves antibody structure prediction by modeling side chain conformations, outperforming existing methods that lack side chain context.

bioRxiv · Sep 22, 2021Biophysics

Antibody structure prediction using interpretable deep learning

J. A. Ruffolo, J. Sulam, J. J. Gray

This paper introduces DeepAb, an interpretable deep learning method for predicting antibody FV structures from sequence, showing improved accuracy and providing insights into residue interactions.

bioRxiv · May 27, 2021Biophysics

Geometric Potentials from Deep Learning Improve Prediction of CDR H3 Loop Structures

J. A. Ruffolo, C. Guerra, S. P. Mahajan, J. Sulam, J. J. Gray

DeepH3 improves CDR H3 loop structure prediction by learning geometric potentials from antibody sequences, outperforming standard Rosetta energy functions in benchmark tests.

bioRxiv · Feb 9, 2020Biophysics