Keywords: [ ensemble learning ]
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, or dataset biases, such that the models memorize dataset-specific biomolecule properties instead of learning affinity prediction rules. As a result, the prediction performance of models drops for unseen biomolecules. Here, we present DebiasedDTA, a novel drug-target affinity (DTA) prediction model training framework that addresses dataset biases to improve affinity prediction for novel biomolecules. DebiasedDTA uses ensemble learning and sample weight adaptation to identify and avoid biases and is applicable to most DTA prediction models. The results show that DebiasedDTA can boost models while predicting the interactions between unseen biomolecules. In addition, prediction performance for seen biomolecules also improves when the surface patterns are debiased. The experiments also show that DebiasedDTA can avoid biases of different sources and augment DTA prediction models of different input and model structures. An open-source python package, pydta, is published to facilitate the adoption of DebiasedDTA by future DTA prediction studies. Out-of-the-box, pydta allows debiasing custom DTA prediction models with only two lines of code and eliminates two sources of bias. pydta is designed to be the go-to library for model debiasing in the field of computational drug discovery.