We present two atom-based machine learning (ML) models for estimating nucleophilicity and electrophilicity by predicting methyl cation affinities (MCAs) and methyl anion affinities (MAAs). The ML models are trained on quantum chemistry (QM)-calculated data of neutral drug-like molecules generated with our recently introduced QM-based workflow. 22 Thus, the ML models are designed to replicate QM-calculated MAA and MCA values, while significantly reducing the computational costs. The Pearson correlation coefficients between the QM-calculated and ML-predicted MCA and MAA values for the held-out test set are 0.97 and 0.95, respectively. In addition, the ML models are accompanied by uncertainty quantification (UQ) predictions, which enable the detection of out-of-sample data points. This UQ approach is intended to inform about when it is recommended to run the QM-based workflow to obtain reliable results. For example, we show that the approach can detect data points from small non-drug-like molecules with a non-zero formal charge, which resulted in large errors due to the molecules being quite different from the underlying training data.