Summary of Gx2mol: De Novo Generation Of Hit-like Molecules From Gene Expression Profiles Via Deep Learning, by Chen Li et al.
Gx2Mol: De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning
by Chen Li, Yuki Matsukiyo, Yoshihiro Yamanishi
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This deep generative model, Gx2Mol, proposes a novel approach to generate hit-like molecules for drug discovery by leveraging gene expression profiles. It uses a variational autoencoder as a feature extractor to learn the latent features of these profiles, which are then used to produce syntactically valid SMILES strings that satisfy specific conditions. Experimental results show that Gx2Mol can generate new molecules with potential bioactivities and drug-like properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want to find a new medicine for a particular disease. This paper helps by developing a way to create new molecules that could be used as medicines. It uses information from genes and proteins to come up with new molecular structures that might work well against certain diseases. The idea is to use this approach to make it easier and faster to discover new drugs. |
Keywords
» Artificial intelligence » Generative model » Variational autoencoder
