Chemical Reactome Predicts Reactions Via Machine Learning

Jan. 19, 2024
Designing models that learn from large datasets that are similar resolves the low-data challenge.

In collaboration with Pfizer, researchers from the University of Cambridge have introduced a data-driven approach called the chemical 'reactome' to predict molecular reactions, crucial for pharmaceutical discovery. Traditional methods involve computationally expensive simulations, which are often inaccurate. The reactome combines automated experiments with machine learning, analyzing correlations between reactants, reagents and reaction outcomes. Validated on over 39,000 pharmaceutical reactions, it accelerates chemical discovery by uncovering hidden relationships.

“The reactome could change the way we think about organic chemistry,” said Dr Emma King-Smith from Cambridge’s Cavendish Laboratory, the paper’s first author, in a press release. “A deeper understanding of the chemistry could enable us to make pharmaceuticals and so many other useful products much faster. But more fundamentally, the understanding we hope to generate will be beneficial to anyone who works with molecules.” 

In a related paper, published in Nature Communications, the team developed a machine-learning approach that enables chemists to introduce precise transformations to pre-specified regions of a molecule, enabling faster drug design. This addresses challenges in late-stage functionalization reactions, providing better predictability and control.

 

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