Docking Energy refers to the energy associated with the binding or interaction between two molecules, often used in the context of molecular docking studies. Molecular docking is a computational technique used in the field of structural biology and drug discovery to predict the preferred orientation and conformation of one molecule (ligand) when bound to another molecule (receptor). Docking energy is a measure of how well the ligand and receptor molecules fit together in a specific orientation. It is a calculated energy score that reflects the strength of the interaction between the molecules. Lower docking energy scores generally indicate a more favorable and stable binding interaction.


T-Cell Receptor-Peptide Interaction Prediction with Physical Model Augmented Pseudo-Labeling

Predicting the interactions between T-cell receptors (TCRs) and peptides is crucial for the development of personalized medicine and targeted vaccine in immunotherapy. Current datasets for training deep learning models of this purpose remain constrained without diverse TCRs and peptides. To combat the data scarcity issue presented in the current datasets, we propose to extend the training dataset by physical modeling of TCR-peptide pairs. Specifically, we compute the docking energies between auxiliary unknown TCR-peptide pairs as surrogate training labels. Then, we use these extended example-label pairs to train our model in a supervised fashion. Finally, we find that the AUC score for the prediction of the model can be further improved by pseudo-labeling of such unknown TCR-peptide pairs (by a trained teacher model), and re-training the model with those pseudo-labeled TCR-peptide pairs. Our proposed method that trains the deep neural network with physical modeling and data-augmented pseudo-labeling improves over baselines in the available two datasets. We also introduce a new dataset that contains over 80,000 unknown TCR-peptide pairs with docking energy scores.