Graph-based molecular representation learning is crucial for predicting molecular properties but often struggles with capturing complex relationships and relies on limited chemical knowledge. Here, the authors introduce MMFRL, a framework that enhances embedding initialization through relational learning, significantly improving accuracy, robustness, and explainability in molecular property prediction.
- Zhengyang Zhou
- Yunrui Li
- Hao Xu