DebiasedDTA: Model Debiasing to Boost Drug-Target Affinity Prediction. (arXiv:2107.05556v3 [q-bio.QM] UPDATED)

Motivation: Computational models that accurately identify high-affinity
protein-chemical pairs can accelerate drug discovery pipelines. These models,
trained on available protein-chemical interaction datasets, can be used to
predict the binding affinity of an input protein-chemical pair. However, the
training datasets may contain surface patterns, called dataset biases, which
cause models to memorize dataset-specific biomolecule properties, instead of
learning binding mechanisms. As a result, the prediction performance of models
drops for unseen biomolecules. Here, we present DebiasedDTA, a novel
drug-target affinity (DTA) …

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