Summary of Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models For Molecular Design, by a N M Nafiz Abeer et al.
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design
by A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban, Byung-Jun Yoon
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM); 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 The abstract describes a research paper that tackles the issue of uncertainty quantification in deep generative models used for material and drug design. Specifically, it focuses on the junction-tree variational autoencoder (JT-VAE) and proposes a method to estimate the epistemic model uncertainty using the active subspace. This approach does not require changes to the JT-VAE architecture and can be applied to any pre-trained model. The paper’s experiments demonstrate the effectiveness of this uncertainty quantification scheme and its potential impact on molecular optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to better design materials and drugs by using computers to predict what molecules might work best. It talks about a special kind of computer program called a generative model that can make new molecule ideas. But these programs are tricky because they have lots of parts that can be hard to understand. This research makes it easier to see the different possibilities that the computer comes up with, which is important for finding the right solution. |
Keywords
» Artificial intelligence » Generative model » Optimization » Variational autoencoder
