DrugSyn
Drug Synergy Prediction
Task Overview
Synergy is a dimensionless measure of deviation of an observed drug combination response from the expected effect of non-interaction. Synergy can be calculated using different models such as the Bliss model, Highest Single Agent (HSA), Loewe additivity model and Zero Interaction Potency (ZIP). Another relevant metric is CSS which measures the drug combination sensitivity and is derived using relative IC50 values of compounds and the area under their dose-response curves.
Impact
Drug combination therapy offers enormous potential for expanding the use of existing drugs and in improving their efficacy. For instance, the simultaneous modulation of multiple targets can address the common mechanisms of drug resistance in the treatment of cancers. However, experimentally exploring the entire space of possible drug combinations is not a feasible task. Computational models that can predict the therapeutic potential of drug combinations can thus be immensely valuable in guiding this exploration.
Generalization
It is important for model predictions to be able to adapt to varying underlying biology as captured through different cell lines drawn from multiple tissues of origin. Dosage is also an important factor that can impact model generalizability.
Product
Small-molecule.
Pipeline Stage
Activity.
Available Datasets
Usage Example
You can access these datasets using the PyTDC library:
from tdc_ml.multi_pred import DrugSyn
# Load a dataset
data = DrugSyn(name='OncoPolyPharmacology')
# Access the data
df = data.get_data()
print(df.head())
# Get train/val/test splits
split = data.get_split()
print(split)