Multimodal fusion with relational learning for molecular property prediction

Graph-based molecular representation learning is essential for predicting molecular properties in drug discovery and materials science. Despite its importance, current approaches struggle with capturing the intricate molecular relationships and often rely on limited chemical knowledge during training. Multimodal fusion, which integrates information from graph and other data sources together, has emerged as a promising approach for enhancing molecular property prediction. However, existing studies explore only a narrow range of modalities, and the optimal integration stages for multimodal fusion remain largely unexplored. Furthermore, the reliance on auxiliary modalities poses challenges, as such data is often unavailable in downstream tasks. Here, we present MMFRL (Multimodal Fusion with Relational Learning), a framework designed to address these limitations by leveraging relational learning to enrich embedding initialization during multimodal pre-training. MMFRL enables downstream models to benefit from auxiliary modalities, even when these are absent during inference. We also systematically investigate modality fusion at early, intermediate, and late stages, elucidating their unique advantages and trade-offs. Using the MoleculeNet benchmarks, we demonstrate that MMFRL significantly outperforms existing methods with superior accuracy and robustness. Beyond predictive performance, MMFRL enhances explainability, offering valuable insights into chemical properties and highlighting its potential to transform real-world applications in drug discovery and materials science.