Summary of Alignment Is Key For Applying Diffusion Models to Retrosynthesis, by Najwa Laabid et al.
Alignment is Key for Applying Diffusion Models to Retrosynthesis
by Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM)
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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 This research paper proposes a novel approach to retrosynthesis, which involves identifying precursors for a given molecule. The authors frame this task as a conditional graph generation problem and leverage diffusion models to tackle it. They show that previous denoisers used in graph diffusion models are limited by their permutation equivariance requirement, which hinders the expressiveness of these models. To overcome this limitation, the researchers relax the equivariance requirement, allowing for more flexibility in generating graphs. The proposed approach achieves state-of-the-art results on the USPTO-50k dataset, outperforming both template-free and template-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how to improve a type of AI model called a diffusion model, which is used for creating new molecules. The authors found that previous models were limited because they had to be symmetrical when generating graphs. They changed the model so it only needs to be symmetrical in certain places, making it better at solving this problem. This new approach does well on a big dataset of molecule structures and has potential applications like interactive planning. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
