## 4 Summary  
Our study proposed a new tool supporting the early stages of drug discovery. The DRSA method brings a vital instrument capable of creating well-performing, efficient, and easily interpretable models. Conceptually, they are of the same class as the Lipinski RO5 or Ghose filter. However, they are inferred systematically, assuring efficient knowledge extraction from data. Such rules can potentially improve the quality of the virtual search by increasing the quality of the filter or targeting specific molecular properties. In the application described here [50], the classical RO5 could be replaced with its DRSA analog, which could bring a better selection of the compounds for further processing. As an illustration, we learned the DRSA models from the original data set containing drug and non-drug molecules. The models' performance was estimated on the held-out data, proving superior to their classical counterparts and comparable with state-of-the-art machine learning models characterized by low interpretability capabilities. The straightforward nature of the inferred rules strongly supports understanding the predictions and encourages further applications in real-life scenarios. As a further study, we aim to apply the DRSA method to selected data sets containing the biological activity of various receptors and enzymes. We will also collect and explicitly include compounds related to new drug modalities, like peptides, macrocycles, or recently popular proteolysis-targeting chimeras (PROTACs).  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app14219966/s1](https://www.mdpi.com/article/10.3390/app14219966/s1)]([https://www.mdpi.com/article/10.3390/app14219966/s1](https://www.mdpi.com/article/10.3390/app14219966/s1)): A set of all drug and non-drug decision rules induced using the Dominance-based Rough Set Approach.  
Conceptualization, G.M., A.M., M.K. and R.A.B.; Methodology, G.M., A.M., M.K. and R.A.B.; Software, G.M., A.M. and R.A.B.; Validation, G.M. and R.A.B.; Formal analysis, G.M., M.K. and R.A.B.; Investigation, G.M., A.M. and R.A.B.; Resources, M.K.; Data curation, R.A.B.; Writing--original draft, G.M., A.M., M.K. and R.A.B.; Visualization, G.M. and R.A.B.; Supervision, M.K. and R.A.B.; Project administration, M.K. and R.A.B.; Funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
[PERSON] and [PERSON] were supported by the Polish Ministry of Science and Higher Education, grant no. 0311/SBAD/0741. [PERSON] acknowledges support from the Polish National Science Center, grant no. DEC-2019/34/E/HS4/00045. [PERSON] acknowledges support from the Polish National Science Center, grant no. 2019/33/B/NZ7/00795.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found at [[https://www.ebi.ac.uk/chembl/](https://www.ebi.ac.uk/chembl/)]([https://www.ebi.ac.uk/chembl/](https://www.ebi.ac.uk/chembl/)) (accessed on 28 October 2024) and https://cartblanche. docking.org/ (accessed on 28 October 2024).  
\begin{table}
\begin{tabular}{c c c c c c|c|c} \hline  & DRSA & XGB & NN & SVM & KNN & AML & ChP \\ \hline Sensitivity & 0.86 & **0.89** & 0.86 & 0.81 & 0.84 & 0.83 & 0.91 \\ Specificity & **0.96** & 0.95 & 0.91 & 0.93 & 0.92 & 0.90 & 0.94 \\ Precision & **0.96** & 0.95 & 0.91 & 0.93 & 0.92 & 0.90 & 0.94 \\ Accuracy & 0.91 & **0.93** & 0.89 & 0.87 & 0.88 & 0.86 & 0.93 \\ \(F_{1}\) score & 0.91 & **0.93** & 0.88 & 0.87 & 0.88 & 0.86 & 0.93 \\ \hline \end{tabular}
\end{table}
Table 6: Classification quality measures of DRSA and selected ML models. In all cases, the quality measure reflects the mean value of three independent resampling procedures of the non-drug data set while the drug molecule counterpart is kept constant. The bold font indicates the best algorithm given a specific measure.