Summary of Diagnosing and Fixing Common Problems in Bayesian Optimization For Molecule Design, by Austin Tripp et al.
Diagnosing and fixing common problems in Bayesian optimization for molecule design
by Austin Tripp, José Miguel Hernández-Lobato
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Chemical Physics (physics.chem-ph); Machine Learning (stat.ML)
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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 highlights three common pitfalls in Bayesian optimization (BO) that can lead to poor empirical performance. The issues include incorrect prior widths, over-smoothing, and inadequate acquisition function maximization. By addressing these problems, even a basic BO setup can achieve high performance on the PMO benchmark for molecule design. This suggests that BO may be underutilized in the machine learning for molecules community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BO helps create new molecules by choosing the best chemical combinations. But sometimes it doesn’t work well because of three main mistakes: not knowing the right amount to start with, smoothing out the results too much, and not finding the best combination. By fixing these problems, even a simple BO approach can make great molecules. This shows that people might be missing opportunities by not using BO enough. |
Keywords
» Artificial intelligence » Machine learning » Optimization
