  Leveraging artificial intelligence for automatic retrosynthesis speeds up
organic pathway planning in digital laboratories. However, existing deep
learning approaches are unexplainable, like "black box" with few insights,
notably limiting their applications in real retrosynthesis scenarios. Here, we
propose MechRetro, a chemical-mechanism-driven graph learning framework for
interpretable retrosynthetic prediction and pathway planning, which learns
several retrosynthetic actions to simulate a reverse reaction via elaborate
self-adaptive joint learning. By integrating chemical knowledge as prior
information, we design a novel Graph Transformer architecture to adaptively
learn discriminative and chemically meaningful molecule representations,
highlighting the strong capacity in molecule feature representation learning.
We demonstrate that MechRetro outperforms the state-of-the-art approaches for
retrosynthetic prediction with a large margin on large-scale benchmark
datasets. Extending MechRetro to the multi-step retrosynthesis analysis, we
identify efficient synthetic routes via an interpretable reasoning mechanism,
leading to a better understanding in the realm of knowledgeable synthetic
chemists. We also showcase that MechRetro discovers a novel pathway for
protokylol, along with energy scores for uncertainty assessment, broadening the
applicability for practical scenarios. Overall, we expect MechRetro to provide
meaningful insights for high-throughput automated organic synthesis in drug
discovery.
