用扩散模型生成蛋白质，实现物理稳定的自动设计
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
- 基于扩散模型的蛋白质生成方法，具备数学严谨性和生成能力
- 结合E(3)等变性，保持氨基酸构象的物理稳定性
- 适合对蛋白质设计与药物发现有需求的研究者
蛋白质设计长期面临挑战。生成式人工智能在该领域展现出巨大潜力，尤其扩散模型凭借坚实的数学基础和出色的生成能力，在蛋白质设计、肽类生成、药物发现及蛋白-配体相互作用研究中表现突出。本文综述了扩散模型的基本定义与特性，重点分析去噪扩散概率模型（DDPM）与基于得分的生成模型（SGM），其中DDPM是SGM的离散形式。同时探讨其在上述领域的应用，并展望如何通过构建E(3)等变扩散模型，推动自主蛋白质工程的发展。E(3)群包含三维空间中的所有旋转、反射与平移，其等变性可最大限度维持每个氨基酸构象的物理稳定性。
原文摘要 · Abstract (English)
Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generative capabilities, offering unique advantages in certain applications such as protein design. In this review, we first give the definition and characteristics of diffusion models and then focus on two strategies: Denoising Diffusion Probabilistic Models and Score-based Generative Models, where DDPM is the discrete form of SGM. Furthermore, we discuss their applications in protein design, peptide generation, drug discovery, and protein-ligand interaction. Finally, we outline the future perspectives of diffusion models to advance autonomous protein design and engineering. The E(3) group consists of all rotations, reflections, and translations in three-dimensions. The equivariance on the E(3) group can keep the physical stability of the frame of each amino acid as much as possible, and we reflect on how to keep the diffusion model E(3) equivariant for protein generation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。