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Molecular property prediction is becoming increasingly important in drug and material discovery, and many research works have demonstrated the great potential of machine learning techniques, especially deep learning. This paper presents our proposed solution for CCKS-2022 task 8, a chemical domain knowledge-aware framework for multi-view molecular property prediction. As a generative self-supervised approach to molecular graph representation learning, the framework is based on Knowledge-guided Pre-training of Graph Transformer (KPGT), which adopts a graph transformer guided by molecular fingerprint and descriptor knowledge. In the fine-tuning stage, combined with practical prediction problems, we fuse functional group information and chemical element knowledge graphs to predict molecular properties. From the perspective of chemical structure, KPGT provides structural information of …