Summary of Geometry Informed Tokenization Of Molecules For Language Model Generation, by Xiner Li et al.
Geometry Informed Tokenization of Molecules for Language Model Generation
by Xiner Li, Limei Wang, Youzhi Luo, Carl Edwards, Shurui Gui, Yuchao Lin, Heng Ji, Shuiwang Ji
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for generating molecules in 3D space using language models (LMs) is presented, which requires a discrete tokenization of 3D molecular geometries. The proposed method, Geo2Seq, converts molecular geometries into SE(3)-invariant 1D sequences, maintaining geometric and atomic fidelity. This enables the use of LMs for molecular geometry generation, which excels in controlled generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to create molecules in space using computers is being explored. Instead of just looking at the molecules’ shapes, this method breaks them down into a special kind of code that can be read by machines. This code keeps all the important information about the molecule’s shape and atoms. The researchers tested their idea with different computer models and found that it works well for creating new molecules in specific ways. |
Keywords
» Artificial intelligence » Tokenization
