Chemical Property Relation Guided Few-Shot Molecular Property Prediction

The ability of molecular property prediction is of great significance to drug discovery. However, molecular property prediction is essentially a few-shot problem, making it a challenge in deep learning applications in this scenario. Although some deep learning models, such as meta-learning, have been used in few-shot molecular property prediction, they neglect the relationships among different properties. In this paper, we introduce a chemical property relation modeling technique for generating the property relation map, which can guide the few-shot molecular property prediction with positively related properties. Some molecules sharing common properties are firstly picked out. Then, multiple property-aware graph neural networks are trained for extracting molecular representations from those molecules. Next, the Spearman's correlation is adopted to calculate the property relations with the property-aware matrix, which is built by calculating the pairwise similarity of the above molecular representations for each pair of properties. In the few-shot molecular property prediction task, the meta-learning strategy is adopted to learn common prediction knowledge from the meta-training categories, which are provided by the property relations. Extensive experiments on two public few-shot molecular property datasets demonstrate that the positively related properties are beneficial for the target property prediction, while negatively related properties have negative effects. With the guidance of positively related properties, the proposed method outperforms various state-of-the-art methods.