Model Training. All models trained in this work are built on the original Graphormer architecture, built on the PyTorch library. We use our and graph feature encoders ( 3 in Figure ) and chromatographic feature encoders (1,2 in Figure ) to create molecular graphs with node-(i.e., atom) and edge-(i.e., bond) features using the DGL-LifeSci package with dense embeddings (4 in Figure ). These graphs are passed to Graphormer (5 in Figure ) to construct a set of learned node representations and the aggregate mCLS token. This molecular fingerprint is interpreted by the MLP (3 in Figure ), consisting of intermediate linear layers and activation functions, which predicts the final retention time. The best performing model contained eight Graphormer encoder layers with an embedding dimension of 512. A selfattention mechanism with 64 attention heads was employed with an attention dropout of 0.15. Adam was used as the optimizer with 𝛽 1 = 0.9, 𝛽 2 = 0.999, and 𝜖 = 10 -8 . Reversed phase models were trained using an initial learning rate of 1.0 × 10 -4 and HILIC models used a learning rate of 1.5 × 10 -4 . The MLP (decoder) consisted of four linear layers with an output of size 512, interleaved with ReLUs 49 as the activation function. The loss function used in training was the root mean squared error (RMSE). In total, the best performing model had 54,700,619 tunable parameters. All hyperparameters for the best performing model were determined by manual tuning using the validation loss. All models were trained for 250 epochs, with a batch size of 64 and early stopping. All models utilized an 80-10-10 (%) training-validation-test split. All models were trained on a single NVIDIA GeForce RTX 4090 graphics card with 24 GB of VRAM. Training a model with RP retention times takes roughly 7.02 hours to complete and finetuning on HILIC retention times takes roughly 14.7 minutes. One of the major benefits of the machine learning approach for RT prediction is that once a model is trained, calculation of retention times is extremely rapid. Evaluation of the total RP retention time test set (n = 14,482) takes 3.4 minutes, which corresponds to roughly 14 ms per retention time prediction.