Active synthesizability prediction for a virtual space of 395 million catalyst candidates with small data
Year: 2026
Type: preprint
Abstract: N-heterocyclic carbenes (NHCs) are well established as catalysts and ligands in many chemical transformations. Recently, interest has grown in pyridine[1,5]imidazolium salt pre-catalysts, stable precursors to NHCs, because they are relatively easy to synthesize and highly adaptable. This work introduces a data-driven workflow to identify which pyridine[1,5]imidazolium salts are readily synthetically accessible, starting from exploratory synthesis attempts for 127 compounds. Our approach relies on a feedback loop with a supervised machine learning model to estimate and dynamically update the synthesizability for a combinatorial space of about 394 million pre-catalysts derived from commercially available precursors. Our final model estimates that 4.78% of the entire space are feasible, mapping the space of readily accessible candidates and providing a valuable set for future inverse design studies. We validated the prediction performance of our approach based on both literature data and a holdout set occupying extremes of the combinatorial space.
Source: ChemRxiv
Institution: University of Groningen
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221145890