Robust generalization of drug-target affinity (DTA) prediction models is a notoriously difficult problem in computational drug discovery. In this article, we present pydebiaseddta: a computational software for improving the generalizability of DTA prediction models to novel ligands and/or proteins. pydebiaseddta serves as the practical implementation of the DebiasedDTA training framework, which advocates modifying the training distribution to mitigate the effect of spurious correlations in the training data set that leads to substantially degraded performance for novel ligands and proteins. Written in Python programming language, pydebiaseddta combines a user-friendly streamlined interface with a feature-rich and highly modifiable architecture. With this article we introduce our software, showcase its main functionalities, and describe practical ways for new users to engage with it.
Keywords: computational drug discovery; drug–target affinity; importance weighting; out-of-distribution generalization; spurious correlation; virtual drug screening.