Semi-Template Retrosynthesis Prediction with 3D Spatial Structures and Reaction Center Disconnection Rules

Retrosynthesis constructs rational synthetic pathways by predicting reactants from the target product. Previous research utilizing Graph Neural Networks often relies on two-dimensional molecular representations. The two-dimensional representations of functional groups of the same type are identical, and ignoring differences in their three-dimensional(3D) structures can cause confusion in identifying reaction centers. Additionally, they disconnect the reaction centers, hindering the accurate identification of chemical transformation rules. To address these issues, we propose the Retro3D model based on a semi-template-based method, integrating 3D structural information to enhance molecular representation. Due to the discrepancies in bond lengths and bond angles of the same type of functional groups in different 3D spatial structures, our method improves the accuracy of identifying reaction centers in the target product. Furthermore, our method determines whether reaction centers are disconnected, differentiating between types of reaction centers to obtain two types of chemical transformation rules. Given the known reaction class, experiments demonstrate that our model achieves superior accuracy in predicting reaction centers and final reactants compared to previous benchmark models. In summary, our methodology aligns with established chemical principles, enhancing its interpretability and broad applicability across diverse synthetic challenges.