Summary of A Framework For Conditional Diffusion Modelling with Applications in Motif Scaffolding For Protein Design, by Kieran Didi et al.
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design
by Kieran Didi, Francisco Vargas, Simon V Mathis, Vincent Dutordoir, Emile Mathieu, Urszula J Komorowska, Pietro Lio
First submitted to arxiv on: 14 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Many generative models have been developed for protein design applications, such as designing binders or enzymes. One key challenge is to precisely scaffold a structural motif, which requires modeling paradigms that can accurately capture complex molecular structures. Denoising diffusion processes have shown promise in addressing this challenge and have achieved early experimental success in certain cases. However, the conditional generation protocols used in these approaches are often motivated heuristically and lack a unified framework, making it difficult to compare and contrast different methods. This paper provides a new perspective on conditional training and sampling procedures by unifying them under a common framework based on Doob’s h-transform. This framework allows for connections between existing methods to be drawn and the development of new protocols that outperform standard approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Protein design is like trying to build a puzzle, where you need to fit different pieces together just right. One way scientists try to do this is by using computers to generate new molecules that have the properties they want. But it’s hard to get these molecules to look like what they should be! That’s why researchers are looking at special computer models called denoising diffusion processes. These models can help create new molecules by “scaffolding” a structural motif, which is like giving the puzzle pieces a framework to fit together correctly. The problem is that scientists don’t always know how these models work or why they’re successful in some cases but not others. This paper helps solve this mystery by showing how different computer models can be connected and improved using a special mathematical trick called Doob’s h-transform. |
Keywords
* Artificial intelligence * Diffusion
