Upon evaluation of the PCM models on the two datasets, they exhibited good performance (Table ). The first dataset, "Auto", contained only data points with pchembl values automatically calculated by ChEMBL (6510 data points). The second dataset, "Manual", included additional data points with standard values but missing pchembl values as they were labeled "Outside Typical Range" by ChEMBL. Adding these manually calculated pchembl values increased the dataset by 307 data points, slightly improved the Q 2 values, and, more importantly, added a new target. Hence, we proceeded with the manual dataset. Predictions with an error greater than two log units were considered outliers. In our evaluation, the Random Forest model showed seven outliers, while the XGBoost model showed 19 outliers (Figure ). Notably, all seven outliers from the random forest model were also present in the XGBoost model. Nine outliers in the test set were molecules already present in the training set but had activities on different targets, with a significant difference in activity between those targets. As an example, "CHEMBL571536" (Figure ) is present in the test set with a pIC50 value of 4.76 against SLC6A2 (NET), and a predicted pIC50 value of 7.62. This error of 2.86 log units might be due to the presence of the same compound in the training set with a higher pIC50 value of 8.0 against SLC6A4. Nine out of the total 19 outliers follow this pattern. To determine if the PCM model is effective in virtual screening situations, we utilized the realistic split method recommended by Martin et.al. This approach involves assigning the most dissimilar group of compounds to the test set to mimic screening scenarios where the screened chemical space is typically different from the one used to train the model. The difference between the test set in the realistic split and the random-stratified split can be observed by using t-SNE for chemical space visualization, as shown in Figure . In Figure , one can observe the random nature of the test set as it covers the chemical space of the entire data set, as opposed to Figure , where the largest compound clusters make up the training set, leaving the singlet compounds and smallest clusters to make up the test set.