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On October 22, 2026, the artificial intelligence model Pinal, built jointly by a research team at Westlake University with The Hong Kong University of Science and Technology (Guangzhou) and the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences, was made public, and the related results were published online in the journal Nature. The model requires only a single sentence of text description to generate candidate proteins that differ from known natural protein sequences and may possess practical functions. The team used protein three-dimensional structures as an intermediate bridge to construct a synthetic training dataset covering approximately 135 million protein sequences and approximately 1.7 billion "protein-text" pairs; the model first generates a structural blueprint from the text, then arranges amino acids and scores the candidate sequences. In four types of experiments—fluorescent proteins, enzymes that degrade PET plastic, alcohol dehydrogenases, and shuttle H proteins—the model designed proteins that performed the target functions. The researchers stated that this direction is still at the proof-of-concept stage, and fine-grained functional design such as specifying emission wavelengths remains a challenge.
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Westlake University and other teams build protein-generating AI model Pinal
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Oct 22
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Westlake University team publishes Pinal model in Nature: a single sentence can generate candidate proteins
A research team from Westlake University, in collaboration with the Hong Kong University of Science and Technology (Guangzhou) and the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences, built an AI model called Pinal that, with just a single sentence of text description, can generate candidate proteins whose sequences differ from known natural proteins and which may possess actual functions. The related results were published online in the journal Nature. Using protein three-dimensional structures as an intermediate bridge, the team constructed a synthetic training dataset covering approximately 135 million protein sequences and approximately 1.7 billion protein-text pairs. The model first generates a structural blueprint based on the text, then arranges amino acids and scores the candidate sequences; in four types of experiments—fluorescent proteins, enzymes that degrade PET plastic, alcohol dehydrogenases, and shuttle H proteins—it designed proteins that performed the target functions. The researchers stated that this direction is still at the proof-of-concept stage, and fine-grained functional design such as specifying emission wavelengths remains a challenge.