Author

Haonan Duan

University of TorontoORCID 0000-0002-3608-4467Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:protein sequence· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:experimental assay· 1 Organism:human· 1 Approach:large language model· 1 Organism:multiple species· 1

Frequent Co-Authors

Chris J. MaddisoniD· 2 shared Abhinav AdduriiD· 1 shared Adibvafa FallahpouriD· 1 shared Alán Aspuru‐GuzikiD· 1 shared Alexander A. NevueiD· 1 shared Amirfaham Fallahpour· 1 shared Andrea Francesca SalvadoriD· 1 shared Antonio Cembellin PrietoiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersMolecular Biology

Chris J. MaddisonORCID

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Author profile →

2 papers

Recent·Most cited

BioReason-Pro: Advancing Protein Function Prediction with Multimodal Biological Reasoning

Adibvafa Fallahpour, Arman Seyed-Ahmadi, Parsa Idehpour, Omar Ibrahim, Bong Gun Choi, Purav Gupta, Jack Naimer, Kevin Zhu, Abhinav Adduri, Arnav Shah, Shihao Ma, Talu Güloglu, Nuo Liu, Haotian Cui, Arihant Jain, , Bo Wang

A reasoning LLM combining ESM3 embeddings with GO-GPT term predictions produces protein function annotations that experts rated tie-or-better than UniProt in 79% of cases (SFT model).

bioRxiv · Jul 19, 2026 · PreprintMolecular Biology✦ In-depth digest

Boosting the Predictive Power of Protein Representations with a Corpus of Text Annotations

Haonan Duan, Marta Skreta, Leonardo Cotta, Ella Miray Rajaonson, Nikita Dhawan, Alán Aspuru‐Guzik, Chris J. Maddison

This study shows that fine-tuning protein models on text annotations improves their predictive power for function prediction tasks, outperforming BLAST in some cases.

Nature Machine Intelligence · Aug 18, 2025Peer-reviewedBiochemistry