Here, the summation runs over all unique pairs of samples in the training data. This loss function penalizes the model only when the predicted difference in half-lives between two molecules is smaller than the true difference. By avoiding penalties for cases where the predicted difference exceeds the true difference, the loss function reduces the risk of overfitting and encourages correct rank ordering of molecule stabilities.