Proteina: Scaling Flow-based Protein Structure Generative Models Article Swipe

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Tomas Geffner , Kieran Didi , Zuobai Zhang , Danny Reidenbach , Zhonglin Cao , Jason Yim , Mario Geiger , Christian Dallago , Emine Küçükbenli , Arash Vahdat , Karsten Kreis ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2503.00710

Recently, diffusion- and flow-based generative models of protein structures have emerged as a powerful tool for de novo protein design. Here, we develop Proteina, a new large-scale flow-based protein backbone generator that utilizes hierarchical fold class labels for conditioning and relies on a tailored scalable transformer architecture with up to 5x as many parameters as previous models. To meaningfully quantify performance, we introduce a new set of metrics that directly measure the distributional similarity of generated proteins with reference sets, complementing existing metrics. We further explore scaling training data to millions of synthetic protein structures and explore improved training and sampling recipes adapted to protein backbone generation. This includes fine-tuning strategies like LoRA for protein backbones, new guidance methods like classifier-free guidance and autoguidance for protein backbones, and new adjusted training objectives. Proteina achieves state-of-the-art performance on de novo protein backbone design and produces diverse and designable proteins at unprecedented length, up to 800 residues. The hierarchical conditioning offers novel control, enabling high-level secondary-structure guidance as well as low-level fold-specific generation.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2503.00710
PDF: https://arxiv.org/pdf/2503.00710
OA Status: green
Cited By: 1
OpenAlex ID: https://openalex.org/W4415082968

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DOI: https://doi.org/10.48550/arxiv.2503.00710

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Title: Proteina: Scaling Flow-based Protein Structure Generative Models

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Language: en

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Publication year: 2025

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Publication date: 2025-03-02

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Authors: Tomas Geffner, Kieran Didi, Zuobai Zhang, Danny Reidenbach, Zhonglin Cao, Jason Yim, Mario Geiger, Christian Dallago, Emine Küçükbenli, Arash Vahdat, Karsten Kreis

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Landing page: https://arxiv.org/abs/2503.00710

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Cited by: 1

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Citations by year (recent): 2025: 1

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