## 2 Materials and Methods  
This work considers a carefully created data set containing drug and non-drug molecules. On the one hand, we have extracted all approved drugs from the most recent version of the ChEMBL database (version 3.3) [24]. After removing the proteins, oligosaccharides, oligonucleotides, and molecules belonging to the class \"unknown\" and applying the standardization procedure on a SMILES representation of molecules, we ended up with 2197 compounds reflecting the so-far registered small-molecule drugs. On the other hand, the non-drug component was derived from the ZINC-22 database containing ca. 37 billion commercially available, enumerated, and searchable compounds [25]. To ensure this component comprises the same number of molecules as its drug counterpart, we sampled ZINC-22 uniformly. Since such a choice is non-unique, we repeated the sampling three times to estimate the influence of the composition of non-drug components on the quality of the resulting models. The final data sets comprised 4394 compounds containing both drug and non-drug components. They were subsequently randomly split into training and test sets, where the size of the latter component was set to 20%. The construction of the data sets was conducted while bearing in mind the study's central aim, which was to compare the existing decision analysis and machine learning approaches with the classical procedures. Therefore, we intentionally limited the training data set while leaving only small molecules and considering the same descriptor space as in the RO5 and the Ghose filter.  
The data sets were subsequently structured using DRSA. It is an extension of the seminal work of [PERSON] [26], handling the inconsistencies typical in multi-criteria decision-aiding processes [27]. Then, the approach leads to rules of the type _\"if..., then...\"_. They capture the conditions (i.e., values of one or more attributes) capable of distinguishing molecules representing two classes, drug and non-drug molecules, hence resembling the classical druggability rules. Therefore, they can be considered their natural extension obtained in a systematic, objective, and efficient way. This algorithm has been widely applied in domains such as finance [28], urban development [29], sustainability [30], energy [31],portfolio optimization [32], and risk assessment [33]. Due to the lack of strict monotonicity of the data, we used a variant of DRSA supporting local monotonicity changes [34].  
To obtain the rules, we first calculated the appropriate molecular descriptors. Here, we applied the Mordred [35] and RDkit [36] libraries. We aimed to separately consider the performance of the DRSA model against the Lipinski RO5 and Ghose filter. To achieve that, we used suitable sets of molecular descriptors in accordance with both reference approaches. For the comparison with Lipinski RO5, we provided the data spanned by the following features: molecular weight (MWt), number of HB donors (nHdon), number of HB acceptors (nHacc), and octanol-water partition coefficient (\(\log P\)). To confront the performance of the DRSA model with the Ghose procedure, we utilized the data set containing molecular weight (MWt), molar refractivity (MR), number of non-hydrogen atoms (nAtoms), and again octanol-water partition coefficient (\(\log P\)). In both cases, the DRSA rules were inferred with the DOMLEM algorithm [37] and then used to classify molecules from the test set [38]. The following subsection provides a detailed description of the applied method.