Context-informed few-shot molecular property prediction via heterogeneous meta-learning

Abstract

Molecular property prediction is essential in diversified applications, as it helps identify molecules with the desired characteristics. However, the task often suffers from limited data, making the few-shot learning challenging. We introduce a Context-informed Few-shot Molecular Property Prediction via a Heterogeneous Meta-Learning approach, which employs graph neural networks combined with self-attention encoders to effectively extract and integrate both property-specific and property-shared molecular features, respectively. Based on the property-shared molecular features, we further infer molecular relations by using an adaptive relational learning module. The final molecular embedding is improved by aligning with the property label in the property-specific classifier. Furthermore, we employ a heterogeneous meta-learning strategy that updates parameters of the property-specific features within individual tasks in the inner loop and jointly updates all parameters in the outer loop. This enhances the model's ability to effectively capture both general and contextual information, leading to a substantial improvement in predictive accuracy. The model's performance was rigorously evaluated across various real molecular datasets, showcasing its superiority over current methods, especially in challenging few-shot learning scenarios.