从热力学到蛋白质设计：面向自主蛋白质工程的生物分子生成扩散模型
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
AI总结：
本文综述扩散模型的数学原理及其在蛋白质、肽和药物相关生物分子生成中的应用，并探讨 E(3) 等变性与自主蛋白质工程的未来方向。
摘要：
数十年来，设计具有理想性质的蛋白质一直是一项重大挑战。生成式人工智能是一种很有前景的方法，并已在多种蛋白质生成任务中取得巨大成功。值得注意的是，扩散模型凭借其坚实的数学基础和出色的生成能力脱颖而出，在蛋白质设计等特定应用中具有独特优势。在本综述中，我们首先给出扩散模型的定义和特征，随后重点讨论两种策略：Denoising Diffusion Probabilistic Models（去噪扩散概率模型，DDPM）和 Score-based Generative Models（基于分数的生成模型，SGM），其中 DDPM 是 SGM 的离散形式。此外，我们讨论了它们在蛋白质设计、肽生成、药物发现以及蛋白质-配体相互作用中的应用。最后，我们概述了扩散模型在推动自主蛋白质设计与工程方面的未来前景。E(3) 群由三维空间中的所有旋转、反射和平移组成。E(3) 群上的等变性能够尽可能保持每个氨基酸框架的物理稳定性，并且我们思考了如何使用于蛋白质生成的扩散模型保持 E(3) 等变。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
