Evaluating the trained models is critical to assess accuracy and reliability beyond its training data. In the context of binding affinity prediction, the evaluation was conducted regarding the model's predictive accuracy and ability to rank ligands based on their affinities. We conducted this evaluation using a hold-out set indicates no correlation, and 1 indicates a perfect positive correlation (when one value increases, the other also increases) . A higher (𝜌) signifies that the model has a strong ranking capacity. The coefficient was calculated on the test with a value of 0.66. The encouraging 𝜌 score indicates that the model has effectively learned relevant patterns from the training data, allowing it to provide accurate predictions . A more in-depth examination of the residuals (see Figure ) reveals a consistent positive correlation. About Distribution of the difference between the experimental and calculated binding affinity