Summary of Retro-prob: Retrosynthetic Planning Based on a Probabilistic Model, by Chengyang Tian and Yangpeng Zhang and Yang Liu
Retro-prob: Retrosynthetic Planning Based on a Probabilistic Model
by Chengyang Tian, Yangpeng Zhang, Yang Liu
First submitted to arxiv on: 25 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The proposed succinct probabilistic model addresses the uncertainty in retrosynthetic planning by describing the probability of reactions used. This uncertainty is caused by hallucinations from backward models. The model is then utilized to develop a new algorithm called retro-prob, which maximizes the successful synthesis probability of target molecules. Retro-prob achieves high efficiency through the application of the chain rule of derivatives. Experimental results on the Paroutes benchmark demonstrate that retro-prob outperforms previous algorithms, including retro* and retro-fallback, in terms of speed and plan quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to plan synthetic routes for molecules. This process is called retrosynthesis, and it’s important for making new medicines and other products. However, there was a problem with the current methods – they didn’t always work well because they relied on guessing which reactions would be successful. The scientists created a new model that takes into account the uncertainty of these reactions. They used this model to develop an algorithm called retro-prob, which can plan synthetic routes more efficiently and accurately than before. |
Keywords
» Artificial intelligence » Probabilistic model » Probability
