## 3 Results and Discussion  
### Overview of Decision Rules Obtained with DRSA  
Each DRSA model was trained on 3515 compounds, which constitutes 80% of considered compounds. The rest was held out and used for model quality estimation. There were three independent resampling procedures of the non-drug set, each followed by the application of the DRSA rule inference algorithm. The drug molecule counterpart was kept the same. For example, the resulting DRSA\({}_{1}\) model comprises 402 decision rules (they are all provided in the online Supplementary Materials). Among them, 164 are responsible for drug case classification, whereas the remaining 238 support the non-drug cases. Although the number of rules is relatively high, they are simpler than the already-known alternatives. Most are based on three or fewer conditions while using at most two descriptors. Only around 15% of rules involve more than four conditions and less than 3% more than five conditions. Some are generic enough to support hundreds of cases, while others cover only a few training observations. Removing the weakest rules simplifies the DRSA model without significantly influencing the performance on a test set. For instance, excluding the rules covering at most five training examples reduces the total number of rules from 402 to 275, with only a slight effect of the sensitivity decrease by 1%. The condition parts of the most supported rules for each class, along with their coverage, are presented in Table 3. Each row of the table represents a conjunction of conditions on the upper or lower limits of descriptor values that should be coupled with a decision part in the form of either \"then _drug_\" or \"then _non-drug_\". In addition, the strongest drug rule (the first row in Table 3a)  
if MW \(\leq\) 242.1 and nHBDon \(\geq\) 1, then _drug_,  
covering 302 drug cases is presented in Figure 1a, and the strongest non-drug rule (the first row in Table 3b)  
if MW \(\geq\) 388.2 and 2.6 \(\leq\) SLogP \(\leq\) 3.5 and nHBAcc \(\leq\) 4 and nHBDon \(\leq\) 1, then _non-drug_, covering 143 non-drug cases is shown in Figure 1b. Figure 1: Graphical representations of the strongest rules for both classes. The colored areas reflect the supported descriptor ranges. The first _drug_ rule imposes restriction exclusively on two descriptors: nHBAcc and nHBDon. It supports the cases with relatively low molecular weights and at least one HB donor. The second _drug_ rule, of almost the same strength, additionally involves the octanol-water partition coefficient and enforces moderate lipophilicity to classify a molecule as a drug. On the other hand, the strongest _non-drug_ rule involves all four descriptors. It assumes that the compounds of non-drug nature are relatively heavy (MW \(\geq\) 388.2), of moderate lipophilicity lying in a relatively narrow range, and have at most 1 and 4 HB donors and acceptors, respectively. Some of these conditions are intuitively obvious. For instance, molecular weight apparently influences the classification process, which is clearly reflected in non-overlapping conditions involving this attribute. Similarly, restricted ranges of the number of HB donors and acceptors of the strongest _non-drug_ rule can be related to severe limitations to establishing the HB interactions between these compounds and enzymes or receptors, which is often necessary to achieve biological activity. Interpreting some other conditions, e.g., those involving narrow ranges of the SlogP descriptor, is more challenging and requires training examples. Independent of this, the predictions based on decision rules can easily be understood and interpreted. Their transparent nature supports the trust, possibly encouraging society to adopt the use of DRSA models. In particular, each prediction can be connected to the analysis of supporting rules. For example, let us consider the case of Xylometazoline, a widely applied drug that reduces the symptoms of nasal congestion.