Single-cell Drug-Target Interaction Benchmark Group

We formalize the predictive, non-generative, task definition as a function taking a given protein, cell, and drug as input to output a score representing a probability of drug-target binding. More details to-be-announced.

A dataset containing drug-target interactions at single-cell resolution for various contextualized proteins and diseases must be constructed to train a model for this task. In TDC-2, we assembeled a dataset containing data points for Rheumatoid Arthritis and Inflammatory Bowel Disease. More details to-be-announced.

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

from tdc.benchmark_group import scdti_group
group = scdti_group.SCDTIGroup()
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
out = 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.

The evaluation metric is AUC. More details to-be-announced.