Applicability domain-expansion studies for machine learning models reveal new inhibitors of CYP2B6.

ABSTRACT: CYP2B6 is an important enzyme in the phase 1 metabolism of key pharmaceuticals, and inhibition of this enzyme can lead to adverse drug events. Machine learning models can potentially predict interactions with CYP2B6; however, there is limited data with which to train these models in the public domain. We proposed enhancing the applicability domain and improving the predictive capability of our CYP2B6 inhibition model by selecting a small, diverse set of compounds to test in vitro and adding the results to our model training set. We used a distance-based approach to define the applicability domain of the model and then measured the chemical diversity by creating t-distributed stochastic neighbor embedding plots to represent the chemical space of our model. After comparing this chemical spac

SUBMITTER: Vignaux PA 

PROVIDER: S-EPMC12713527 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature