Summary of Chemistry-inspired Diffusion with Non-differentiable Guidance, by Yuchen Shen et al.
Chemistry-Inspired Diffusion with Non-Differentiable Guidance
by Yuchen Shen, Chenhao Zhang, Sijie Fu, Chenghui Zhou, Newell Washburn, Barnabás Póczos
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a novel approach to conditional generation of molecules using diffusion models. By leveraging domain knowledge from quantum chemistry as a non-differentiable oracle, the authors can guide an unconditional diffusion model without requiring large labeled datasets. The oracle provides estimated gradients that allow the diffusion process to sample from a conditional distribution specified by quantum chemistry. This method demonstrates improved precision in generating novel and stable molecular structures. Experimental results show that it significantly reduces atomic forces, enhancing the validity of generated molecules for stability optimization, and is compatible with both explicit and implicit guidance in diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers create new molecules by using a special kind of computer program called a “diffusion model”. The program can make guesses about what a molecule should look like based on some rules from chemistry. Normally, these programs need lots of labeled data to work well, but this paper shows that we don’t always need that much data. Instead, the program uses some smart tricks to guess what a good molecule looks like. This helps computers create new molecules that are stable and have the right properties. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Optimization » Precision
