Taking a broader perspective, the model does not consistently perform poorly for any specific pattern or cluster of proteins. Nonetheless, we garnered insightful findings related to protein families, resulting in improved accuracy. Table reports the number of entries of the most significant protein families, the count of predictions falling within a ±1 error range, and the corresponding percentage of predicted values within this error range . Among the protein families analyzed, the hydrolase family is the predominant one for which the model exhibits good performance, with 26 out of 57 predictions (45.61%) accurately falling within the predefined ±1 error range. The Transferase family also displayed a promising outcome, with 15.30% of predictions falling within this error range. Furthermore, the Lyase family demonstrated high predictive accuracy, with 9 out of 16 predictions (56.25%) aligning within the desired error range. Similarly, the Chaperone and Transcription protein families achieved satisfactory results, with 33.34% and 50.00% of predictions within the ±1 error range, respectively. Included in SI Figure is a comprehensive plot showcasing the data for all other families. In line with our strategy of predicting binding affinity through protein-ligand interactions, we also calculated the Root Mean Squared Error (RMSE) and Pearson correlation coefficient. This allowed us to bench-December 15, 2023 mark our results against documented outcomes in the literature that employed the GCN architecture. Notably, the reference for comparison originates from the CASF-2016 set and is referred here as GCN-NET PLIG . The comparison can be made because CASF-2016 is representative of the PDBBbind dataset . Table provides the results, computed as the mean of individual point predictions for each data point within the test set. The GCNNet-PLIG model showcased a Pearson correlation coefficient of 0.76, indicating a meaningful and positive linear relationship between its predicted and actual values. In contrast, the InterGraph model demonstrated a slightly lower but comparable coefficient of 0.60. Although our model displayed a relatively weaker correlation (𝜌), it is important to highlight that it is still positive and not random.