Towards Building Reliable Conditional Diffusion Models for Protein Generation
Keywords: Protein Generation
Abstract: Generating novel and functional protein sequences is critical to a wide
range of applications in biology. Recent advancements in conditional diffusion models have shown impressive empirical performance in protein generation tasks. However, reliable generations of protein remain an open research question in de novo protein design, especially when it comes to conditional diffusion models. Considering the biological function of a protein is determined
by multi-level structures, we propose a novel multi-level conditional diffusion model that integrates both sequence-based
and structure-based information for efficient end-to-end protein design guided by
specified functions. By generating representations at different levels simultaneously, our framework can effectively model the inherent hierarchical relations between different levels, resulting in an informative and
discriminative representation of the generated protein. We also propose a Protein-MMD, a new reliable evaluation metric, to evaluate the quality of generated protein with conditional diffusion models. Our new metric is able to capture both distributional and functional similarities between real and generated protein sequences while ensuring conditional consistency. We experiment with standard datasets and the
results on protein generation tasks demonstrate the efficacy of the proposed generation framework and evaluation metric.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 8193
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