This editorial presents an analysis of the articles published in the Journal of Cheminformatics Special Issue "AI in Drug Discovery". We review how novel machine learning developments are enhancing structural-based drug discovery; providing better forecasts of molecular properties while also improving various elements of chemical reaction prediction. Methodological developments focused on increasing the accuracy of models via pre-training, estimating the accuracy of predictions, tuning model hyperparameters while avoiding overfitting, in addition to a diverse range of other novel and interesting methodological aspects, including the incorporation of human expert knowledge to analysing the susceptibility of models to adversary attacks, were explored in this Special Issue. In summary, the Special Issue brought together an excellent collection of articles that collectively demonstrate how machine learning methods have become an essential asset in modern drug discovery, with the potential to advance autonomous chemistry labs in the near future.