Summary of Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge, by Yizhen Luo et al.
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge
by Yizhen Luo, Kai Yang, Massimo Hong, Xing Yi Liu, Zikun Nie, Hao Zhou, Zaiqing Nie
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Biomolecules (q-bio.BM)
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 The paper presents MV-Mol, a novel molecular representation learning model that combines chemical structures, biomedical texts, and knowledge graphs to capture multi-view molecular expertise. The approach utilizes text prompts to incorporate view information and design a fusion architecture to extract view-based representations. The model is pre-trained on heterogeneous data of varying quality and quantity using a two-stage procedure. The paper demonstrates the effectiveness of MV-Mol in molecular property prediction and multi-modal comprehension of molecular structures and texts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study develops a new way to learn about molecules, combining different sources of information like chemical structures, medical text, and scientific knowledge graphs. This approach allows for a more comprehensive understanding of molecules and can be used to predict their properties or understand how they interact with other molecules. |
Keywords
* Artificial intelligence * Multi modal * Representation learning
