Summary of Cross-modal Learning For Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning, by Sakhinana Sagar Srinivas et al.
Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning
by Sakhinana Sagar Srinivas, Venkataramana Runkana
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 a novel framework called Multi-Modal Fusion (MMF) that combines Graph Neural Networks (GNNs) with Large Language Models (LLMs) to enhance performance in predicting molecular properties. The MMF approach leverages the strengths of both GNNs and LLMs, improving accuracy and robustness while reducing overfitting risk. This is achieved by synergistically harnessing the analytical capabilities of GNNs and the linguistic generative and predictive abilities of LLMs. The framework showcases its efficacy in surpassing state-of-the-art baselines on benchmark datasets for property prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper’s main idea is to create a new way to predict properties of molecules using both graph neural networks (GNNs) and large language models (LLMs). It combines the strengths of these two approaches to make better predictions. This helps with accuracy, reducing the risk of the model becoming too specialized to one specific task. |
Keywords
» Artificial intelligence » Multi modal » Overfitting
