Protein design requires a deep understanding of the inherent complexities of
the protein universe. While many efforts lean towards conditional generation or
focus on specific families of proteins, the foundational task of unconditional
generation remains underexplored and undervalued. Here, we explore this pivotal
domain, introducing DiMA, a model that leverages continuous diffusion on
embeddings derived from the protein language model, ESM-2, to generate amino
acid sequences. DiMA surpasses leading solutions, including autoregressive
transformer-based and discrete diffusion models, and we quantitatively
illustrate the impact of the design choices that lead to its superior
performance. We extensively evaluate the quality, diversity, distribution
similarity, and biological relevance of the generated sequences using multiple
metrics across various modalities. Our approach consistently produces novel,
diverse protein sequences that accurately reflect the inherent structural and
functional diversity of the protein space. This work advances the field of
protein design and sets the stage for conditional models by providing a robust
framework for scalable and high-quality protein sequence generation.

利用连续扩散和蛋白质语言模型 ESM-2 推导的嵌入，在无条件生成方面超越了当前的解决方案，进一步评估了生成的蛋白质序列的质量、多样性、分布相似性和生物相关性，并提供了一个可扩展且高质量的蛋白质序列生成框架，从而推动了蛋白质设计领域的发展。

利用语言模型嵌入进行蛋白质序列生成的扩散

Diffusion on language model embeddings for protein sequence generation

Protein engineering seeks to identify protein sequences with optimized
properties. When guided by machine learning, protein sequence generation
methods can draw on prior knowledge and experimental efforts to improve this
process. In this review, we highlight recent applications of machine learning
to generate protein sequences, focusing on the emerging field of deep
generative methods.

该论文综述了近期将机器学习应用于蛋白质序列优化的发展，重点介绍了深度生成方法对这一过程的促进作用。