## 3 Results and Discussion
### Comparison of DRSA with Selected Machine Learning Methods  
We also confronted the predictive capabilities of DRSA with six selected popular ML methods. These included the following:  
* eXtreme Gradient Boosting (XGB) [43]--a powerful ML algorithm based on decision trees, using gradient boosting to optimize performance; it focuses on speed and performance through regularization and efficient parallelization; we employed a variant with the number of trees between 10 and 250. * Neural Network (NN) [44]--a classical multilayer perceptron with two hidden layers with the number of neurons between 32 and 512 each, _relu_ activation, _adam_ solver, and L2 regularization. * Support Vector Machine (SVM) [45]--a supervised ML algorithm mapping input data into a higher-dimensional space using a kernel function (in our case, a radial variant), identifying the optimal hyperplane that maximizes the margin between different classes, and using this hyperplane to classify new data points by determining which side of the margin they fall on. * K-nearest neighbors (KNN) [46]--a classification method calculating the distance (in our case, Euclidean) between the alternative to be classified and all reference alternatives and assigning it to the class most common among its nearest \(K\) neighbors (in our case, \(3\leq K\leq 19\)). * AutoML (AML) [47]--a procedure automatically adjusting the entire prediction pipeline, including a classification algorithm and a preprocessing phase using the genetic optimization procedure. * ChemProp (ChP) [48]--a deep learning graph-convolutional Neural Network based on Message Passing Neural Network accompanied by a feature extraction module accepting a chemical molecule as an input. This algorithm uses the full molecule representation, not just simple descriptors like all remaining models used in this study. Therefore, it has an unfair advantage. For that reason, the metrics associated with the model should be considered as model performance at the prediction limit. They are not accounted for selecting the best score, and for clarity, they are separated by a vertical line. The algorithms' hyperparameter values were determined using a Bayesian optimization procedure [49]. The use of all these approaches was preceded by the application of min-max scaling of performances. The results are shown in Table 5. The proposed DRSA algorithm performs comparably to the state-of-the-art ML models. In both data sets, DRSA attained a very high score on specificity and precision while losing to XGB on sensitivity, accuracy, and \(F_{1}\) score. Note that depending on the context, maximizing the score on a different metric might be desirable. Given the obtained results, DRSA seems to be more suitable when false positives are costly (specificity) or their cost is high (precision). In turn, XGB is more adequate when missing positive cases have high consequences (sensitivity), classes are well balanced and false positives and false negatives carry similar costs (accuracy), or there is class imbalance and you want a satisfying trade-off between precision and sensitivity (\(F_{1}\)). We have also investigated the influence of feature space extension on the performance of the DRSA model as well as selected ML approaches. All considered methodologies were applied to data sets containing molecular descriptors related to both the Lipinski and Ghose filters, namely the number of HB donors, the number of HB acceptors, molecular weight, octanol-water partition coefficient (\(\log P\)), molar refractivity, and the number of non-hydrogen atoms. The results are presented in Table 6. As expected, we observe an improvement in model quality in nearly all cases. Specifically, for the DRSA model, sensitivity and specificity increased by 0.06 and 0.01, respectively, compared to the DRSA results obtained using only RO5 descriptors. Similarly, for the ML models, we also note slightly better performance, except for the KNN model trained on Ghose descriptors, where sensitivity and specificity decreased slightly.