A Graph-to-Sequence Method for C-H Activation Retrosynthesis.

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Artificial Intelligence

Background:

• C-H activation reactions are crucial in organic synthesis but challenging for retrosynthesis due to C-H bond inertness and regioselectivity issues.
• Existing computational models struggle to identify reactive sites and predict disconnection pathways in C-H activation systems.

Purpose of the Study:

• To develop a novel, template-free framework for retrosynthesis prediction of C-H activation reactions.
• To improve the accuracy and interpretability of retrosynthesis predictions by modeling molecular graph edits.

Main Methods:

• Proposed TransGraphEdit, a framework combining a graph neural network (GNN) encoder and a Transformer decoder.
• Modeled stepwise structural edits based on the product's molecular graph.
• Employed a SMILES augmentation strategy to enhance generalization for low-resource C-H activation reactions.

Main Results:

• TransGraphEdit effectively captures long-range dependencies and synergistic effects between functional groups.
• Achieved 58.06% Top-1 accuracy on a C-H Arylation dataset and 53.8% on USPTO-50K.
• Demonstrated robustness and domain adaptability in retrosynthesis predictions.

Conclusions:

• TransGraphEdit offers an interpretable and mechanism-aware approach to C-H activation retrosynthesis.
• The framework shows significant promise for advancing computational organic synthesis and reaction prediction.