## 3 Results and Discussion
### Comparison of DRSA with Domain-Specific Methods  
As demonstrated in Table 5, DRSA is superior with respect to the reference approaches proposed by [PERSON] [1] and [PERSON] [3]. In both cases, DRSA reaches all classification quality metrics at the level of at least 80%. For example, the models run on the same attributes as the Ghose filter, which correctly indicated the \"drug\" class for 97% of all molecules they predicted as drugs, as captured by _precision_. They also correctly predicted the class for 91% of molecules overall, whether they were drugs or not, as reflected by _accuracy_. \begin{table}
\begin{tabular}{c c c c c c c} \hline nHBAcc & nHBDon & SLogP & MW & P\({}_{\text{DRSA}}\) & P\({}_{\text{Lip}}\) & P\({}_{\text{Gho}}\) \\ \hline \multicolumn{8}{c}{Example drug: Xylometazoline (CHEMBL312448)} \\ \hline
2 & 1 & 3.15 & 244.19 & 1 & 1 & 1 \\ \hline \multicolumn{8}{c}{Upper} \\ \hline \multicolumn{8}{c}{Example non-drug: ZINCrB00003 GBgp} \\ \hline
3 & 2 & 3.17 & 404.17 & 0 & 1 & 1 \\ \multicolumn{8}{c}{ine Middle} \\ \hline \multicolumn{8}{c}{Complex example non-drug: ZINCrn2000000 hm6C} \\ \hline
7 & 0 & 2.93 & 325.1 & 0 & 1 & 1 \\ \hline \multicolumn{8}{c}{Bottom} \\ \hline \end{tabular}
\end{table} 
Table 4: Three exemplary compounds from the test set representing the drug and non-drug cases. The upper part reflects the drug molecule, whereas the middle and bottom sections represent the non-drug examples. On the contrary, the Lipinski RO5 and Ghose rules perform poorly on these real-world data. Both marked a vast majority of the non-drug molecules as active. In particular, the Lipinski rules misclassified over 99% non-drug examples, hence attaining extremely low scores on the specificity metric. In turn, the Ghose filter attained less favorable results on the remaining four metrics (e.g., its sensitivity was lower by 0.16 while its \(F_{1}\) score was worse by 0.06). Such unfavorable performance of the classical techniques could be anticipated given the simplicity of individual rules defined by only four conditions. The DRSA rules are more capacious and thus capable of extracting the patterns from provided data more thoroughly. The DRSA models perform well in specificity, while a higher false negative rate causes a slightly worse absolute sensitivity value (though still better than for the classical rules). This means that DRSA will tend to miss a small fraction of real drugs rather than classifying non-drug molecules as drugs. Such conditioning of the problem has certain benefits. Massive application of the DRSA model to a large number of molecules will less often bring false positive cases, which may reduce the costs of experimental verification of the molecule's activity. Interestingly, DRSA attained better results on all metrics when run on the attribute set applied by the Ghose procedure rather than Lipinski RO5. For example, the average accuracy increased from 0.87 to 0.91 while the \(F_{1}\) score rose from 0.86 to 0.91. This shows further potential for improving the method's performance when supplied with richer, more adequate information. The DRSA models can easily be applied to support the early-stage drug discovery process. As with any other statistical model, it can be trained on a selected set of compounds for which the experimental data exist. Subsequently, carefully created and--what is equally essential--thoroughly validated models can expose new, unseen compounds to deliver predictions and their intuitive interpretations. Due to relative numerical simplicity and clear potential for parallelization, these models can handle ultra-large, enumerated chemical databases containing synthesizable compounds, such as Enamine REAL, WuXi, or e-Molecules. It is also worth mentioning that the presented methodology can naturally address new chemical modalities involving the compounds that are beyond Rule of Five (bRO5). In particular, DRSA can be applied to data associated with RNA therapeutics, protein degraders, cyclopeptides, and other classes of molecules [40; 41; 42].