Summary of Learning Molecular Representation in a Cell, by Gang Liu et al.
Learning Molecular Representation in a Cell
by Gang Liu, Srijit Seal, John Arevalo, Zhenwen Liang, Anne E. Carpenter, Meng Jiang, Shantanu Singh
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM)
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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 Information Alignment (InfoAlign) approach leverages the information bottleneck method to learn molecular representations that capture biological responses to small molecule perturbations. By integrating molecules and cellular response data into a context graph, InfoAlign optimizes latent representations to discard redundant structural information while aligning with feature spaces from neighboring nodes. This approach outperforms existing contrastive methods in downstream applications such as molecular property prediction against 27 baseline methods across four datasets and zero-shot molecule-morphology matching. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to learn about how small molecules affect cells. It creates a special kind of map that connects the molecules with what happens inside the cells when they’re exposed to these molecules. This helps remove unnecessary information and focus on what’s important. The result is better predictions for how well a drug will work and if it might cause side effects. The approach was tested and showed promise in two different areas: predicting properties of small molecules and matching shapes of molecules with cell structures. |
Keywords
* Artificial intelligence * Alignment * Zero shot
