Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning

• URL: http://arxiv.org/abs/2408.14964v1
• Date: Tue, 27 Aug 2024 11:10:39 GMT
• Title: Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning
• Authors: Sakhinana Sagar Srinivas, Venkataramana Runkana,
• Abstract summary: We introduce a Multi-Modal Fusion (MMF) framework that harnesses the analytical prowess of Graph Neural Networks (GNNs) and the linguistic generative and predictive abilities of Large Language Models (LLMs) Our framework combines the effectiveness of GNNs in modeling graph-structured data with the zero-shot and few-shot learning capabilities of LLMs, enabling improved predictions while reducing the risk of overfitting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the field of chemistry, the objective is to create novel molecules with desired properties, facilitating accurate property predictions for applications such as material design and drug screening. However, existing graph deep learning methods face limitations that curb their expressive power. To address this, we explore the integration of vast molecular domain knowledge from Large Language Models (LLMs) with the complementary strengths of Graph Neural Networks (GNNs) to enhance performance in property prediction tasks. We introduce a Multi-Modal Fusion (MMF) framework that synergistically harnesses the analytical prowess of GNNs and the linguistic generative and predictive abilities of LLMs, thereby improving accuracy and robustness in predicting molecular properties. Our framework combines the effectiveness of GNNs in modeling graph-structured data with the zero-shot and few-shot learning capabilities of LLMs, enabling improved predictions while reducing the risk of overfitting. Furthermore, our approach effectively addresses distributional shifts, a common challenge in real-world applications, and showcases the efficacy of learning cross-modal representations, surpassing state-of-the-art baselines on benchmark datasets for property prediction tasks.

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