Balancing Data on Proteochemometrics Activity Classification

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 datasets used in proteochemometrics 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 evaluated in kinases and GPCRs from BindingDB.

We observed that the predicted proportion of positives was driven by the actual data balance in the test set.

Additionally, it was confirmed that data balance had an impact on the performance estimates of the proteochemometrics model.

We recommend a combination of data augmentation and clustering in the training set (semi_resampling) in order to mitigate the data imbalance effect in a realistic scenario.

The code of this analysis is publicly available at https://github.com/b2slab/imbalance_pcm_benchmark.