Machine learning for active sites prediction of quinoline derivatives

Privileged structures, like quinoline, have diverse biological activities, and their synthetic versatility makes them crucial for drug design. In traditional synthesis methods, the C-H functionalization of quinoline can be effectively achieved using different conditions, especially transition metal catalysis. Machine learning (ML) techniques enable rapid prediction of C-H functionalization, facilitating drug design and development. In this study, a generalizable approach to prediction of site selectivity that is accomplished using an artificial neural network (ANN) for the site prediction of derivatives of quinoline. In an 80/10/10 training/validation /testing split of about 2467 compounds, the model takes as input a SMILES string of a compound and uses six quantum mechanics descriptors to identify reactive site(s) of the compound. On the external validation set, 86% of all molecules were correctly predicted. This model allows chemists to rapidly determine which site is more likely to produce electrophilic substitution reaction.