Knowledge graph-enhanced molecular contrastive learning with functional prompt

Abstract Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient. Many existing methods are purely data driven, focusing on exploiting the intrinsic topology and construction rules of molecules without any chemical prior information. The high data dependency makes them difficult to generalize to a wider chemical space and leads to a lack of interpretability of predictions. Here, to address this issue, we introduce a chemical element-oriented knowledge graph to summarize the basic knowledge of elements and their closely related functional groups. We further propose a method for knowledge graph-enhanced molecular…

• NN
  National Natural Science Foundation of China

  Awards: DH-2022ZY0012, NSFC91846204, ZYYCXTD-D-202002, 202002, NSFCU19B2027
• FR
  Fundamental Research Funds for the Central Universities

  Awards: 226-2022-00226, 202002