Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Autor:	Zhang B; School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China., Zhang X; School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China., Du W; School of Software Engineering, University of Science and Technology of China, Hefei, Anhui 230026, China., Song Z; Hefei JiShu Quantum Technology Co. Ltd., Hefei, Anhui 230026, China., Zhang G; School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China., Zhang G; School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China.; Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China., Wang Y; School of Software Engineering, University of Science and Technology of China, Hefei, Anhui 230026, China., Chen X; Gusu Laboratory of Materials, Suzhou, Jiangsu 215123, China., Jiang J; School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China.; Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China., Luo Y; School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China.; Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China.; Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China.
Jazyk:	angličtina
Zdroj:	Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2022 Oct 11; Vol. 119 (41), pp. e2212711119. Date of Electronic Publication: 2022 Oct 03.
DOI:	10.1073/pnas.2212711119
Abstrakt:	Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach.
Databáze:	MEDLINE
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