In this work, we introduce two new atom-based ML models designed to predict MCA and MAA values for estimating nucleophilicity and electrophilicity. Our ML models are trained on QM-calculated MCA and MAA values of neutral drug-like molecules to ensure that the predictions apply to pharmaceutical research. Furthermore, the ML predictions are accompanied by reliable uncertainty estimates, which provide valuable information on when to employ the QM-based workflow. Compared to our QM-based workflow, the ML models significantly reduce the computational cost, achieving a median wall time of 0.36 seconds per molecule for the same 2,341 molecules on a single CPU core. This makes the ML approach particularly useful as a post-filtering method in retrosynthesis planning, where it can quickly detect potential selectivity issues and thereby improve synthetic route design.