The initial dataset of 3700 formulations was used for generating regression models for LC and LE values, as well as classification models for four different threshold values respectively (LC ≥10, 20, 30 or 40 %, as well as LE ≥20, 40, 60 or 80 %), using the R package caret 6.0-91 (Figure ). For this purpose, formulations were first split by stratified random sampling into a training (TS, 80 %) and an external holdout set (HS, 20 %), retaining a similar class ratio within the HS as within the whole dataset. For each splitting process during this work, we followed the ''mixtures-out" approach , ensuring that all mixtures encompassing the same types of A blocks, B blocks and (co-formulated) drugs, differing only in polymer-drug mass ratios, block lengths or measurement times, were kept within the same set to prevent data leakage.