Machine learning for active sites prediction of quinoline derivatives

Abstract

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 synthesis. In this study, a generalizable approach to predict site selectivity is accomplished by using artificial neural network (ANN), which is suitable for the site prediction of derivatives of quinoline. In an 80/10/10 training/validation/testing split of 2467 compounds, the model takes SMILES strings as input format and uses six quantum chemical descriptors to identify reactive site(s) of the compound. On the external validation set, 86 .5% of all molecules were correctly predicted. This model allows chemists to rapidly predict which site is more likely to produce electrophilic substitution reaction.