Overall, tree-based model types (RF and XGB) and support vector classifiers achieved the best nMCC CV scores, reaching median values of 0.78 (Figure ). When comparing the different descriptor subsets, RDK7 fingerprints, similar to the results of the regression models, performed best, with median nMCC CV values of 0.78 across all model types (Figure ) and 0.81 for the best models of each investigated threshold (Figure ). However, differences between median values of most subsets are small, suggesting that each set is able to capture, to some extent, important structural elements for determining drug loading. The descriptor subset m2v performed worst, which might be at least partly due to the way the vector embeddings were mixed (weighted by molar fractions). Substituting m2v vectors with pb2v vector embeddings for the polymer of each formulation increased performance. We hypothesize that these language model-based approaches could be further improved, e.g. by adjusting the Morgan circular fingerprint radius for m2v descriptors or accounting for the invariance to block copolymeric compositions of the polyBERT fingerprints (as discussed in ). The calculated SiRMS descriptors did not outperform the more conventional molecular descriptors and fingerprints often used for QSPR studies of small molecules. However, as described in the Methods section, this could be partly contributed to the alternative computation of atomic properties with the aim