Summary of Preference Optimization For Molecule Synthesis with Conditional Residual Energy-based Models, by Songtao Liu et al.
Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models
by Songtao Liu, Hanjun Dai, Yue Zhao, Peng Liu
First submitted to arxiv on: 4 Jun 2024
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 The proposed framework uses conditional residual energy-based models (EBMs) to predict synthetic routes in drug discovery. The current data-driven strategies employ one-step retrosynthesis models and search algorithms, which have limitations in molecule synthesis route generation due to a greedy selection of the next molecule set without any lookahead. The new approach focuses on the quality of the entire synthetic route based on specific criteria like material costs, yields, and step count. This framework can enhance the quality of synthetic routes generated by various strategies in a plug-and-play fashion. The results show that this method can consistently boost performance across different strategies and outperforms previous state-of-the-art top-1 accuracy by 2.5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to predict how to make medicines using machine learning. They used a special kind of model called an energy-based model, which helps them generate the best possible recipe for making a medicine. This approach is better than previous methods because it considers different factors like how much something costs and how well the final product works. The results show that this new method is very effective at predicting the best way to make medicines. |
Keywords
» Artificial intelligence » Energy based model » Machine learning
