Summary of Molx: Enhancing Large Language Models For Molecular Learning with a Multi-modal Extension, by Khiem Le et al.
MolX: Enhancing Large Language Models for Molecular Learning with A Multi-Modal Extension
by Khiem Le, Zhichun Guo, Kaiwen Dong, Xiaobao Huang, Bozhao Nan, Roshni Iyer, Xiangliang Zhang, Olaf Wiest, Wei Wang, Nitesh V. Chawla
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes an approach to improve the performance of Large Language Models (LLMs) in chemistry-related tasks by enhancing their ability to comprehend molecules. The current limitations of LLMs are attributed to their reliance on common textual representations, such as SMILES strings, which do not fully capture the complexity of molecular structures. To address this issue, the authors introduce a multi-modal external module called MolX, which combines fine-grained features extracted from both SMILES string and 2D molecular graph representations with a handcrafted molecular fingerprint. The proposed method is evaluated on four downstream molecule-related tasks, including molecule-to-text translation and retrosynthesis, demonstrating superior performance over baselines with minimal additional trainable parameters. By leveraging domain knowledge and pre-training the model with a diverse set of tasks, MolX enhances the capabilities of LLMs in chemistry, opening up new avenues for applications such as molecule design, synthesis planning, and property prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research improves how Large Language Models (LLMs) understand molecules. Currently, these models are not very good at this task because they rely on simple text-based representations that don’t capture the complexity of molecular structures. The authors create a new module called MolX to help LLMs better understand molecules by combining different types of data and domain-specific knowledge. They test MolX on four molecule-related tasks and find that it performs much better than existing methods with only a small amount of additional information. This research can lead to breakthroughs in areas like designing new molecules, planning their synthesis, and predicting their properties. |
Keywords
» Artificial intelligence » Multi modal » Translation
