Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning

Late-stage functionalization (LSF) is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, an LSF platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as a critical step in LSF, the computational model correctly predicted the reactivity of 81% of novel substrates, while reaction yields for diverse reaction conditions were predicted with a mean absolute error margin of 4–5%. The regioselectivity of the major products was accurately captured in up to 90% of the cases studied. When applied to 23 diverse commercial drug molecules, the platform successfully identified numerous opportunities for structural diversification. The influence of steric and quantum mechanical information on model performance was quantified and a new comprehensive simple user-friendly reaction format (SURF) is introduced which proved to be a key enabler for seamlessly integrating deep learning and high-throughput experimentation (HTE) for LSF.