Dual-loss framework for accurate binding affinity prediction in biological complexes

A processor-implemented method, comprising:

• predicting a binding affinity for a first molecule and a second molecule in a biological complex using a machine learning model, wherein the machine learning model comprises a model trained to generate the predicted binding affinity based on a first loss associated with a labeled training data set and a second loss associated with an unlabeled training data set generated from the labeled training data set;
• determining that the predicted binding affinity for the first molecule and the second molecule in the biological complex exceeds a threshold binding affinity;
• and
• based on the determining, performing one or more actions with respect to the first molecule and the second molecule.

A processing system, comprising:

• at least one memory having executable instructions stored thereon;
• and
• one or more processors configured to execute the executable instructions to cause the processing system to:
• generate an unlabeled training data set based on random perturbation applied to biological complexes included in a labeled training data set, wherein:
• the labeled training data set includes a plurality of samples of biological complexes, and
• each respective sample of a biological complex is associated with a calculated binding affinity between molecules in the respective sample;
• train a predictive model to predict binding affinity of a first molecule to a second molecule based on a first loss associated with the labeled training data set and a second loss associated with the unlabeled training data set;
• and
• deploy the trained predictive model.