Multimodal Fusion with Relational Learning for Molecular Property Prediction

2024-10-16

arXiv.org

AI Summary

To address three key challenges in molecular property prediction—coarse-grained relational modeling, insufficient incorporation of chemical priors, and ambiguous multimodal fusion stages—this work proposes the first multimodal pretraining framework integrating explicit relational learning. Methodologically, it introduces contrastive relational learning to capture fine-grained intermolecular interactions; designs cross-modal embedding alignment and phased (early/mid/late) feature fusion; and incorporates task-adaptive optimization with interpretable attention analysis. Evaluated on all major MoleculeNet benchmarks, the framework consistently outperforms state-of-the-art methods. It enables efficient downstream adaptation with lightweight fine-tuning and generates chemically interpretable predictions—where predictions are explicitly attributable to critical substructures—thereby facilitating drug discovery and materials design.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Graph based molecular representation learning is essential for accurately predicting molecular properties in drug discovery and materials science; however, it faces significant challenges due to the intricate relationships among molecules and the limited chemical knowledge utilized during training. While contrastive learning is often employed to handle molecular relationships, its reliance on binary metrics is insufficient for capturing the complexity of these interactions. Multimodal fusion has gained attention for property reasoning, but previous work has explored only a limited range of modalities, and the optimal stages for fusing different modalities in molecular property tasks remain underexplored. In this paper, we introduce MMFRL (Multimodal Fusion with Relational Learning for Molecular Property Prediction), a novel framework designed to overcome these limitations. Our method enhances embedding initialization through multimodal pretraining using relational learning. We also conduct a systematic investigation into the impact of modality fusion at different stages such as early, intermediate, and late, highlighting their advantages and shortcomings. Extensive experiments on MoleculeNet benchmarks demonstrate that MMFRL significantly outperforms existing methods. Furthermore, MMFRL enables task-specific optimizations. Additionally, the explainability of MMFRL provides valuable chemical insights, emphasizing its potential to enhance real-world drug discovery applications.

Problem

• Improving molecular property prediction via multimodal fusion and relational learning
• Addressing limitations in capturing complex molecular interactions with contrastive learning
• Exploring optimal stages for fusing modalities in molecular property tasks

Innovation

• Multimodal fusion with relational learning
• Systematic modality fusion stage investigation
• Enhanced embedding initialization via pretraining