Develop
Developability Prediction
Task Overview
Immunogenicity, instability, self-association, high viscosity, polyspecificity, or poor expression can all preclude an antibody from becoming a therapeutic. Early identification of these negative characteristics is essential. This task is to predict the developability from the amino acid sequences.
Impact
A fast and reliable developability predictor can accelerate the antibody development by reducing wet-lab experiments. They can also alert the chemists to foresee potential efficacy and safety concerns and provide signals for modifications. Previous works have devised accurate developability index based on 3D structures of antibody. However, 3D information are expensive to acquire. A machine learning that can calculate developability based on sequence information is thus highly ideal.
Generalization
The model is expected to be generalized to unseen classes of antibodies with various structural and functional characteristics.
Product
Antibody.
Pipeline Stage
Efficacy and safety.
Available Datasets
Usage Example
You can access these datasets using the PyTDC library:
from tdc_ml.single_pred import Develop
# Load a dataset
data = Develop(name='TAP')
# Access the data
df = data.get_data()
print(df.head())