We introduce ADMETrix, a de novo drug design framework that combines the generative model REINVENT with ADMET AI, a geometric deep learning architecture for predicting pharmacokinetic and toxicity properties. To our knowledge, this is the first integration enabling real-time generation of small molecules optimized across multiple ADMET endpoints. We evaluate our method in two settings: (i) multi-parameter optimization of ADMET and physicochemical properties, and (ii) scaffold hopping to reduce toxicity while preserving key pharmacophoric features. Using the GuacaMol benchmark, we provide the first systematic evaluation of REINVENT in a multi-objective ADMET context, demonstrating its advantages in generating drug-like, biologically relevant molecules. The code is available at https://github.com/n-mourdou/ADMETrix .