Abstract

Structural diversification of lead molecules is a key component of drug discovery to explore close-in chemical space. Late stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made significant strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines message-passing neural network and an 13C NMR-based transfer learning to predict the atom-wise probabilities of functionalization. We validated our model retrospectively and with a series of prospective experiments, showing that it accurately predicts the outcomes of Minisci-type and P450 transformations, outperforming state-of-the-art Fukui-based reactivity indices.