For the trained initial GIN model prior to transfer learning, performed well on the validation set, with a R 2 of 0.91, MAE of 0.024 min, and RMSE of 0.034 min. These positive results gave us confidence that the initial model had learned how to effectively associate the chemical information present in graph inputs of analyte structures to their respective retention times. To preserve these learned representations, many of the model parameters were frozen during the transfer learning process. Frozen components included the first two GIN layers/blocks in entirety, and the first hidden layer of the third GIN layer/block. However, while using the Adam optimizer from pre-training, loss per epoch would dramatically fluctuate and was unable to converge. This issue was resolved by testing various optimizers, batch sizes, and learning rates for training as the model architecture cannot be modified during transfer learning, with averaged stochastic gradient descent yielding smooth loss curves and predictive models.