HTS
High-Throughput Screening
Task Overview
High-throughput screening (HTS) is the rapid automated testing of thousands to millions of samples for biological activity at the model organism, cellular, pathway, or molecular level. The assay readout can vary from target binding affinity to fluorescence microscopy of cells treated with drug. HTS can be applied to different kinds of therapeutics however most available data is from testing of small-molecule libraries. In this task, a machine learning model is asked to predict the experimental assay values given a small-molecule compound structure.
Impact
High throughput screening is a critical component of small-molecule drug discovery in both industrial and academic research settings. Increasingly more complex assays are now being automated to gain biological insights on compound activity at a large scale. However, there are still limitations on the time and cost for screening a large library that limit experimental throughput. Machine learning models that can predict experimental outcomes can alleviate these effects and save many times and costs by looking at a larger chemical space and narrowing down a small set of highly likely candidates for further smaller-scale HTS.
Generalization
The model should be able to generalize over structurally diverse drugs. It is also important for methods to generalize across cell lines. Drug dosage and measurement time points are also very important factors in determining the efficacy of the drug.
Product
Small-molecule.
Pipeline Stage
Activity - hit identification.
Available Datasets
Usage Example
You can access these datasets using the PyTDC library:
from tdc_ml.single_pred import HTS
# Load a dataset
data = HTS(name='HIV')
# Access the data
df = data.get_data()
print(df.head())