Multi-Modal Fusion Frameworks of Subgraph-Optimized Graph Autoencoder for Molecular Property Prediction
Kaiyuan Zhang, Congyu Han, Fenghua Zhang, Cheng Lin, Quanlong Li, Tianyi Zang, Yanli Zhao
TL;DR
This paper introduces a new framework for predicting molecular properties using subgraph-optimized graph autoencoders and multimodal fusion strategies.
Contribution
The novel contribution is the development of TurboGAE, a subgraph-optimized graph autoencoder, and effective multimodal fusion strategies for molecular property prediction.
Findings
TurboGAE effectively captures substructure features impacting molecular properties.
Multimodal fusion strategies align intermodal features during pretraining, leveraging each modality's strengths.
The proposed methods show excellent performance on downstream molecular property prediction tasks.
Abstract
Molecular property prediction refers to predicting the properties of a given molecular representation. This task is of great significance in fields such as drug design and has garnered widespread attention from researchers. For molecular property prediction, the quality of feature learning plays a decisive role in model performance. Although existing molecular graph models can extract effective feature representations from graph structures, how to better utilize these features across different learning tasks remains an important challenge. This paper proposes a subgraph-optimized Graph Autoencoder (TurboGAE) and several multimodal fusion strategies. By introducing a subgraph-level graph tokenizer, TurboGAE more effectively captures the impact of substructure features (within molecular structures) on molecular properties. For cross-modal molecular features, a rational and effective…
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Taxonomy
TopicsComputational Drug Discovery Methods · Advanced Graph Neural Networks · Machine Learning in Materials Science