Protein-Peptide Interaction Benchmark Group (+TCR-Epitope)

TDC-2 introduces the Protein-Peptide Binding Affinity prediction task. The predictive, non-generative task is to learn a model estimating a function of a protein, peptide, antigen processing pathway, biological context, and interaction features. It outputs a binding affinity value or binary label indicating strong or weak binding. The binary label can also include additional biomarkers, such as allowing for a positive label if and only if the binding interaction is specific.
To account for additional biomarkers beyond binding affinity value, our task is specified with a binary label.
TDC-2 provides datasets and benchmarks for a generalized protein-peptide binding interaction prediction task and a TCR-Epitope binding interaction prediction task.

To access a generalized protein-peptide benchmark in the group, use the following code:

from tdc.benchmark_group.protein_peptide_group import ProteinPeptideGroup
group = ProteinPeptideGroup()
train, val = group.get_train_valid_split() # val dataset will be empty. use the train dataset if fine-tuning desired.
test = group.get_test()

## --- (optional) train your model --- ##

predictions = model.predict(test)  # modify as per your model code and test output
out = group.evaluate(predictions)

To access a TCR-Epitope benchmark in the group, use the following code:

from tdc.benchmark_group.protein_peptide_group import ProteinPeptideGroup
group = TCREpitopeGroup()
train, val = group.get_train_valid_split() 
test = group.get_test()

## --- train your model --- ##

predictions = model.predict(test)  # modify as per your model code and test output
res = group.evaluate(predictions)

Follow the instructions on how to use the BenchmarkGroup class and obtain training, validation, and test sets, and how to submit your model to the leaderboard.