Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions.

ABSTRACT: While in the last years there has been a dramatic increase in the number of available bioassay datasets, many of them suffer from extremely imbalanced distribution between active and inactive compounds. Thus, there is an urgent need for novel approaches to tackle class imbalance in drug discovery. Inspired by recent advances in computer vision, we investigated a panel of alternative loss functions for imbalanced classification in the context of Gradient Boosting and benchmarked them on six datasets from public and proprietary sources, for a total of 42 tasks and 2 million compounds. Our findings show that with these modifications, we achieve statistically significant improvements over the conventional cross-entropy loss function on five out of six datasets. Furthermore, by employing these

SUBMITTER: Boldini D 

PROVIDER: S-EPMC9650867 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature