Here 𝑵 denotes the total number of ligand-protein complexes, 𝒚 𝒊 is the actual value of binding affinity for each complex and ŷ 𝐢 is the predicted binding affinity for each ligandprotein complex. Minimizing 𝑳𝒐𝒔𝒔 𝒈 contributes to correctly predicting the ligand-protein affinity. 𝑳𝒐𝒔𝒔 𝒓𝒆𝒈 and 𝒘 𝟐 are the regularization loss and weight, respectively, while L2 regularization loss was here used to prevent overfitting of model: where 𝒕 𝒊 represent the model parameter, 𝒏 is the number of model parameter. The model was trained by using traindbx2 routine (example of a configuration file for traindbx2 in the INI format is provided in Figure ). Training was performed with a maximum of 200 epochs and early stopping was used by monitoring the total loss on the validation sets for 3 consecutive epochs. The model was trained with mini-batch gradient descent (batch size of 100) and the adaptive moment estimation (ADAM) optimizer with a learning rate of 5e-4 and a decay rate of 0.99.