Posts Tagged ‘diffusion3d’

Diffusion Tutorial

March 1, 2025

Some tutorials on diffusion models:

[An Arxiv Tutorial]
https://arxiv.org/pdf/2403.18103
https://arxiv.org/abs/2403.18103

Chan, S. H. (2024, March 26). Tutorial on diffusion models for imaging and vision. arXiv.org. https://arxiv.org/abs/2403.18103

has master equation, forward & back SDE, relationship of SDE to p(x)

[Also, Some Useful Blogs]
https://baincapitalventures.notion.site/Diffusion-Without-Tears-14e1469584c180deb0a9ed9aa6ff7a4c https://yang-song.net/blog/2021/score/
https://lilianweng.github.io/posts/2021-07-11-diffusion-models/

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Diffusion Tutorial

February 17, 2025

some tutorials on diffusion models:

[An Arxiv Tutorial]
https://arxiv.org/pdf/2403.18103

[Some Useful Blogs]
https://baincapitalventures.notion.site/Diffusion-Without-Tears-14e1469584c180deb0a9ed9aa6ff7a4c https://yang-song.net/blog/2021/score/
https://lilianweng.github.io/posts/2021-07-11-diffusion-models/

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Conformational sampling and interpolation using language-based protein folding neural networks

February 7, 2025

https://www.biorxiv.org/content/10.1101/2023.12.16.571997v1

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Crystal Structure Determination from Powder Diffraction Patterns with Generative Machine Learning | Journal of the American Chemical Society

November 14, 2024

https://pubs.acs.org/doi/10.1021/jacs.4c10244

Riesel, E. A., Mackey, T., Nilforoshan, H., Xu, M., Badding, C. K., Altman, A. B., Leskovec, J., & Freedman, D. E. (2024). Crystal Structure Determination from Powder Diffraction Patterns with Generative Machine Learning. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.4c10244

seems to use a form of “steered diffusion” to match the powder pattern

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A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets

March 29, 2024

https://www.nature.com/articles/s41467-024-46569-1

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Deep learning protein conformational space with convolutions and latent interpolations

December 15, 2023

https://journals.aps.org/prx/abstract/10.1103/PhysRevX.11.011052

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Illuminating protein space with a programmable generative model | Nature

November 23, 2023

https://www.nature.com/articles/s41586-023-06728-8
QT:{{”
Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences, and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, we introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems that enables long-range reasoning with sub-quadratic scaling, layers for efficiently synthesizing three-dimensional structures of proteins from predicted inter-residue geometries and a general low-temperature sampling algorithm for diffusion models.
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Introduction to Diffusion Models for Machine Learning

September 10, 2023

https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction/

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