Author

Kooshiar Azimian

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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Biochemistry· 1 Synthetic Biology· 1

Signature Assays, Organisms & Methods

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Assay:protein sequence· 2 Approach:transformer / foundation model· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:protein design· 1 Research Focus:protein structure prediction· 1 Research Focus:sequence-to-function modeling· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Kathy Y. WeiiD· 2 shared Mohammad S. ParsaiD· 2 shared Devika Prabhakaran· 1 shared Eliot Osher· 1 shared Indran Mathavan· 1 shared Ismail Naderi· 1 shared Jeeven Singh· 1 shared Kiana Azimian· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBiochemistry

Kathy Y. WeiORCID

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2 papers

Recent·Most cited

An Artificial Intelligence Model for Translating Natural Language into Functional de Novo Proteins

Timothy P. Riley, Mohammad S. Parsa, Pourya Kalantari, Ismail Naderi, Kiana Azimian, Nemya Begloo, Oleg S. Matusovsky, Eliot Osher, Devika Prabhakaran, Rahul Yadav, Klem Simelis, Indran Mathavan, Jeeven Singh, Kooshiar Azimian, Kathy Y. Wei

This paper introduces MP4, a transformer-based model that translates natural language into functional de novo proteins, achieving high performance in sequence realism, fold quality, and function alignment.

ACS Synthetic Biology · Aug 10, 2026Peer-reviewedBiochemistry

TaxoFormer: Hierarchical Transformer for Predicting the Full Taxonomic Lineage of Protein Sequences

Mohammad S. Parsa, Kooshiar Azimian, Kathy Y. Wei

The paper introduces TaxoFormer, a hierarchical transformer model that predicts the full taxonomic lineage of protein sequences using a structured tokenization scheme and achieves accurate lineage prediction on a large dataset.

bioRxiv · Jun 9, 2026 · PreprintSynthetic Biology