Summary of Towards 3d Molecule-text Interpretation in Language Models, by Sihang Li et al.
Towards 3D Molecule-Text Interpretation in Language Models
by Sihang Li, Zhiyuan Liu, Yanchen Luo, Xiang Wang, Xiangnan He, Kenji Kawaguchi, Tat-Seng Chua, Qi Tian
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR); 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 3D-MoLM model enables language models to interpret and analyze 3D molecular structures by integrating a 3D molecular encoder with the LM’s input space. This integration is achieved through a 3D molecule-text projector, allowing for cross-modal molecular understanding and instruction following. The model is evaluated on various downstream tasks, including molecule-text retrieval, captioning, and open-text QA tasks, significantly surpassing existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer that can understand and analyze molecules like a scientist! This new language model, called 3D-MoLM, helps computers comprehend three-dimensional molecular structures. It does this by combining two important parts: a way to represent 3D molecules and a language model. The result is a powerful tool that can answer complex questions about molecules and even generate text descriptions of them. |
Keywords
* Artificial intelligence * Encoder * Language model
