To evaluate the accuracy of this model in actual virtual screening scenarios, we partitioned the data using the realistic split proposed by Martin et al. This method applies a hierarchical clustering procedure to the data set based on Morgan FingerPrint (bits=1024, radius=2) which is then sorted in descending order based on cluster size. The first 80% of the ordered dataset, representing the largest clusters of compounds with similar characteristics, is selected to form the training set, leaving small clusters or standing-alone compounds to form the test set. Therefore, this approach emphasizes the novelty of the test set compared to the training set by reducing their similarity. This method splits the data solely based on molecular structures without considering target stratification. As a result, the test set was missing one target, namely SLC6A14, the Amino Acid Transporter B 0,+ (ATB 0,+ ). With its 9 data points, this target was fully included in the training set.