2025/01/05 by W. B. Li, Xavier F. Cadet, Li, Wen-ran +15 · 3 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Medicine · #Artificial Intelligence (cs.AI) #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Microbial Metabolic Engineering and Bioproduction #Monoclonal and Polyclonal Antibodies Research #Quantitative Methods (q-bio.QM) #thermodynamics and calorimetric analyses
paper · pdf · doi:10.48550/arxiv.2501.02680
openalex publication_date 2025/01/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.