1 Introduction Molecular Property Prediction aims to identify molecules sharing similar efficacious properties[1],which is a foundational task in drug discovery,materials science and bioinformatics.Graph neural networ...1 Introduction Molecular Property Prediction aims to identify molecules sharing similar efficacious properties[1],which is a foundational task in drug discovery,materials science and bioinformatics.Graph neural networks(GNNs)have shown significant success in this field.However,GNN-based methods often face label scarcity,limiting their performance in predicting molecular properties.Besides,GNNs trained on specific datasets frequently struggle with generalization due to domain shift[2].展开更多
基金sponsored in part by the National Key Research and Development Program of China(No.2023YFB3307500)the Science and Technology Innovation Project of Hunan Province(No.2023RC4014)the National Natural Science Foundation of China(NSFC)(Grant Nos.62076146,62021002,U20A6003,6212780016).
文摘1 Introduction Molecular Property Prediction aims to identify molecules sharing similar efficacious properties[1],which is a foundational task in drug discovery,materials science and bioinformatics.Graph neural networks(GNNs)have shown significant success in this field.However,GNN-based methods often face label scarcity,limiting their performance in predicting molecular properties.Besides,GNNs trained on specific datasets frequently struggle with generalization due to domain shift[2].
