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Informative representation of molecules is a crucial prerequisite in AI-driven drug design and discovery. Pharmacophore information including functional groups and chemical reactions can indicate molecular properties, which have not been fully exploited by prior atom-based molecular graph representation. To obtain a more informative representation of molecules for better molecule property prediction, we propose the Pharmacophoric-constrained Heterogeneous Graph Transformer (PharmHGT). We design a pharmacophoric-constrained multi-views molecular representation graph, enabling PharmHGT to extract vital chemical information from functional substructures and chemical reactions. With a carefully designed pharmacophoric-constrained multi-view molecular representation graph, PharmHGT can learn more chemical information from molecular functional substructures and chemical reaction information. Extensive downstream experiments prove that PharmHGT achieves remarkably superior performance over the state-of-the-art models the performance of our model is up to 1.55% in ROC-AUC and 0.272 in RMSE higher than the best baseline model) on molecular properties prediction. The ablation study and case study show that our proposed molecular graph representation method and heterogeneous graph transformer model can better capture the pharmacophoric structure and chemical information features. Further visualization studies also indicated a better representation capacity achieved by our model.

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The Shandong Peninsula is located in northern China and is bordered by the Bohai Sea and the Yellow Sea (Figure 1a). The coastal waters of the Shandong Peninsula are forced by the East Asian monsoon system (Huang et al., 2012), land runoff and coastal currents, namely, the Bohai Coastal Current, the Lubei Coastal Current, and the Yellow Sea Coastal Current, which flow clockwise around the peninsula. In China, the coastal waters of the Shandong Peninsula are intensively maricultured areas (Figure 1b; Ren et al., 2019;

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