Summary of Data-efficient Molecular Generation with Hierarchical Textual Inversion, by Seojin Kim et al.
Data-Efficient Molecular Generation with Hierarchical Textual Inversion
by Seojin Kim, Jaehyun Nam, Sihyun Yu, Younghoon Shin, Jinwoo Shin
First submitted to arxiv on: 5 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Molecular Networks (q-bio.MN)
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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 paper introduces Hierarchical textual Inversion for Molecular generation (HI-Mol), a novel data-efficient molecular generation method that tackles the challenge of developing an effective framework with limited molecular data. Inspired by hierarchical information, HI-Mol uses multi-level embeddings to reflect coarse- and fine-grained features in molecule distribution, achieving data-efficient image generation via textual inversion. Compared to conventional methods, HI-Mol learns low-shot molecule distributions and generates molecules through interpolation of token embeddings. Extensive experiments demonstrate HI-Mol’s superiority with 50x less training data on QM9, and the effectiveness of generated molecules in low-shot molecular property prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HI-Mol is a new way to create molecules quickly and efficiently. Right now, it takes a lot of time and money to get the right molecule for things like finding new medicines. HI-Mol helps by using special computer tricks to learn about molecules even with very little data. It’s like trying to understand a big picture by looking at small details first, then putting them together. This method is better than others because it can create good molecules with much less information. It also works well for predicting what properties molecules have. |
Keywords
» Artificial intelligence » Image generation » Token
