A 3D, Structure-Based, Deep Learning Approach for Predicting the Regioselectivity of Transition-Metal Catalysis

Rational development of transition-metal catalysts, even when guided by theory and mechanistic knowledge, involves significant trial and error. Although ML offers the potential to accelerate catalyst discovery and optimization, accurately modeling the complex structures of catalysts and the multistep mechanisms by which they react remains challenging with the limited sets of data available. Olefin hydroformylation is a quintessential example of this challenge: its catalytic cycle involves many, often reversible, steps, and decades of study have not yielded reliable structure-selectivity relationships. We report Libra-ML, a 3D structure-based deep learning approach for predicting experimental outcomes of transition-metal catalyzed reactions. To demonstrate the ability of Libra-ML to model the outcomes of complex catalytic reactions, we predicted the regioselectivity of hydroformylation with terminal olefins catalyzed by rhodium complexes. Comparisons to existing methods demonstrate the state-of-the-art performance of Libra-ML and illustrate the importance of capturing 3D structure to predict experimental outcomes with molecular catalysts.