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From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering

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Title: From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
Authors: Li, Wen-ran, Cadet, Xavier F., Medina-Ortiz, David, Davari, Mehdi D., Sowdhamini, Ramanathan, Damour, Cedric, Li, Yu, Miranville, Alain, Cadet, Frederic
Publication Year: 2025
Collection: Computer Science
Quantitative Biology
Subject Terms: Quantitative Biology - Quantitative Methods, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
More Details: 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.
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2501.02680
Accession Number: edsarx.2501.02680
Database: arXiv
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  Data: From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Wen-ran%22">Li, Wen-ran</searchLink><br /><searchLink fieldCode="AR" term="%22Cadet%2C+Xavier+F%2E%22">Cadet, Xavier F.</searchLink><br /><searchLink fieldCode="AR" term="%22Medina-Ortiz%2C+David%22">Medina-Ortiz, David</searchLink><br /><searchLink fieldCode="AR" term="%22Davari%2C+Mehdi+D%2E%22">Davari, Mehdi D.</searchLink><br /><searchLink fieldCode="AR" term="%22Sowdhamini%2C+Ramanathan%22">Sowdhamini, Ramanathan</searchLink><br /><searchLink fieldCode="AR" term="%22Damour%2C+Cedric%22">Damour, Cedric</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Yu%22">Li, Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Miranville%2C+Alain%22">Miranville, Alain</searchLink><br /><searchLink fieldCode="AR" term="%22Cadet%2C+Frederic%22">Cadet, Frederic</searchLink>
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  Data: 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.
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      – SubjectFull: Computer Science - Machine Learning
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      – TitleFull: From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
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