Joonyoung F. Joung, Mun Hong Fong, Nicholas Casetti, Jordan P. Liles, Ne S. Dassanayake & Connor W. Coley (2025)
Highlighted by Jan Jensen
While the title says reaction mechanism prediction, it's really reaction mechanism-based reaction outcome prediction. The approach uses glow matching (a generalization of diffusion-based approaches) to predict changes to the bond-electron (BE) matrix (basically connectivity matrix with the lone pair electron count on the diagonal), thus ensuring mass and charge conservation because changes in the BE matrix are constrained to sum to 0. The method is trained 1.4 million elementary reaction steps derived primarily from the USPTO dataset.
Recursive predictions yield a complete reaction mechanism step by step, starting from the reactants. (I assume the products are defined as the state where no more changes are predicted.) The method is probabilistic so several different reaction outcomes are possible if the process is repeated, and ranked according to frequency. Another option is to use DFT calculation to rank the different mechanisms.
Like any ML method its applicability is tied to the training set. For example, of 22,000 reactions from patents reported in 2024 that were not assigned a specific reaction class in the Pistachio dataset, the approach successfully recovered products in only 351 cases. However, the authors show that a new reaction class can be added with as few as 32 examples.
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