Summary of Any-property-conditional Molecule Generation with Self-criticism Using Spanning Trees, by Alexia Jolicoeur-martineau et al.
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees
by Alexia Jolicoeur-Martineau, Aristide Baratin, Kisoo Kwon, Boris Knyazev, Yan Zhang
First submitted to arxiv on: 12 Jul 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 This paper presents an extension to a graph generation method called Spanning Tree-based Graph Generation (STGG) for generating novel molecules conditional on one or multiple desired properties. The original STGG approach outperformed state-of-the-art models in unconditional generation of valid molecules. To enable conditional generation, the authors incorporate a Transformer architecture, random masking of properties during training, an auxiliary property-prediction loss, and other improvements to create STGG+. The method achieves state-of-the-art performance on both in-distribution and out-of-distribution conditional generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us generate new molecules that have specific properties. For example, we can ask the model to generate a molecule that is not only valid but also has a certain shape or chemical composition. The authors took an existing method called STGG and made it better by adding a few key components. These improvements let the model learn from its mistakes and create even more realistic molecules. This technology can be used in many fields, including medicine and materials science. |
Keywords
* Artificial intelligence * Transformer
