In this work, we collected the experimental data of several recent publications to create an openly accessible, extended formulation database of drug-loaded pOx/pOzi-based amphiphilic micelles (Table ). These data were harnessed to build classification models with different thresholds for LC and LE that, overall, achieved comparable statistics to models previously built on a subset of the data , with Acc values around 0.8 for CV and subsequent external HS predictions. Instead of SiRMS descriptors, the final selection of models uses molar fraction-weighted mordred descriptors and RDK fingerprints for conveying information on physicochemical properties and important structural elements. The dataset was extended significantly in terms of long-term stability measurements and information on a large variety of polymer compositions. Performing more experimental work, especially in terms of diversifying the selection of drugs for which loading was investigated with multiple polymer compositions, could further improve the modelability of the dataset. Alternatively, as recently demonstrated by Rakhimbekova et al. , if new compounds are selected with the models for experimental screening, active learning approaches on relatively small sets of initial measurements could further optimize the polymer selection process.