Author

Grigory Khimulya

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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Synthetic Biology· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:protein design· 2 Assay:protein sequence· 2 Research Focus:benchmarking· 1 Research Focus:protein structure prediction· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Ethan C. AlleyiD· 2 shared George McDonald ChurchiD· 2 shared Surojit BiswasiD· 2 shared Kevin M. EsveltiD· 1 shared Mohammed AlQuraishiiD· 1 shared

Activity by Year

2019-2021 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 9 mutual co-author links

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2 shared papersSynthetic Biology

Ethan C. AlleyORCID

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2 papers

Recent·Most cited

Low-N protein engineering with data-efficient deep learning

Surojit Biswas, Grigory Khimulya, Ethan C. Alley, Kevin M. Esvelt, George McDonald Church

This paper introduces a data-efficient deep learning approach for protein engineering that uses minimal experimental data to predict and optimize protein function with high throughput.

Nature Methods · Apr 1, 2021Peer-reviewedSynthetic Biology

Unified rational protein engineering with sequence-only deep representation learning

Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, George McDonald Church

This paper introduces UniRep, a deep learning-based representation of protein sequences that enables accurate prediction of protein stability and function while reducing engineering costs.

Nature Methods · Oct 21, 2019Peer-reviewedSynthetic Biology