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arXiv · 2602.02425

Repurposing Protein Language Models for Latent Flow-Based Fitness Optimization

Amaru Caceres Arroyo·Lea Bogensperger·Ahmed Allam·Michael Krauthammer·Konrad Schindler·Dominik Narnhofer
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Abstract

Protein fitness optimization is challenged by a vast combinatorial landscape where high-fitness variants are extremely sparse. Many current methods either underperform or require computationally expensive gradient-based sampling. We present CHASE, a framework that repurposes the evolutionary knowledge of pretrained protein language models by compressing their embeddings into a compact latent space. By training a conditional flow-matching model with classifier-free guidance, we enable the direct generation of high-fitness variants without predictor-based guidance during the ODE sampling steps. CHASE achieves state-of-the-art performance on AAV and GFP protein design benchmarks. Finally, we show that bootstrapping with synthetic data can further enhance performance in data-constrained settings.

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Amaru Caceres Arroyo, Lea Bogensperger, Ahmed Allam, Michael Krauthammer, Konrad Schindler, Dominik Narnhofer. 2026-02-02. Repurposing Protein Language Models for Latent Flow-Based Fitness Optimization. https://arxiv.org/abs/2602.02425

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