Balancing Data on Deep Learning-Based Proteochemometric Activity Classification.

ABSTRACT: In silico analysis of biological activity data has become an essential technique in pharmaceutical development. Specifically, the so-called proteochemometric models aim to share information between targets in machine learning ligand-target activity prediction models. However, bioactivity data sets used in proteochemometric modeling are usually imbalanced, which could potentially affect the performance of the models. In this work, we explored the effect of different balancing strategies in deep learning proteochemometric target-compound activity classification models while controlling for the compound series bias through clustering. These strategies were (1) no_resampling, (2) resampling_after_clustering, (3) resampling_before_clustering, and (4) semi_resampling. These schemas were evaluate

SUBMITTER: Lopez-Del Rio A 

PROVIDER: S-EPMC8594867 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature