Summary of Cell Morphology-guided Small Molecule Generation with Gflownets, by Stephen Zhewen Lu et al.
Cell Morphology-Guided Small Molecule Generation with GFlowNets
by Stephen Zhewen Lu, Ziqing Lu, Ehsan Hajiramezanali, Tommaso Biancalani, Yoshua Bengio, Gabriele Scalia, Michał Koziarski
First submitted to arxiv on: 9 Aug 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 work combines high-content imaging (HCI) with deep learning techniques to predict molecular-phenotype interactions, enhancing drug discovery applications. This novel task focuses on HCI-guided molecular design, where generative models are guided by HCI data. The challenge lies in limited labeled data and high-dimensional readouts. To overcome this, the authors propose an unsupervised multimodal joint embedding approach for GFlowNets, defining a latent similarity as a reward function. This method learns to generate new molecules with similar phenotypic effects without relying on pre-annotated labels. The proposed model demonstrates successful generation of molecules with high morphological and structural similarity to the target, increasing the likelihood of similar biological activity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special imaging techniques and computer learning to help find new medicines. Right now, finding new medicines can be slow and hard because scientists don’t know which proteins are important for a medicine’s effects. This method uses pictures of cells to predict how different molecules will affect those cells. It’s like trying to guess what a person will look like if they grow their hair differently – you can use computer models to make predictions. The paper shows that this approach is good at generating new molecule ideas that might work in the same way as existing medicines. |
Keywords
* Artificial intelligence * Deep learning * Embedding * Likelihood * Unsupervised
