Comparing Machine Learning Models for Aromatase (P450 19A1)

Aromatase, or cytochrome P450 19A1,
catalyzes the aromatization
of androgens to estrogens within the body. Changes in the activity
of this enzyme can produce hormonal imbalances that can be detrimental
to sexual and skeletal development. Inhibition of this enzyme can
occur with drugs and natural products as well as environmental chemicals.
Therefore, predicting potential endocrine disruption via exogenous
chemicals requires that aromatase inhibition be considered in addition
to androgen and estrogen pathway interference. Bayesian machine learning
methods can be used for prospective prediction from the molecular
structure without the need for experimental data. Herein, the generation
and evaluation of multiple machine learning models utilizing different
sources of aromatase inhibition data are described. These models are
applied to two test sets for external validation with molecules relevant
to drug discovery from the public domain. In addition, the performance
of multiple machine learning algorithms was evaluated by comparing
internal five-fold cross-validation statistics of the training data.
These methods to predict aromatase inhibition from molecular structure,
when used in concert with estrogen and androgen machine learning models,
allow for a more holistic assessment of endocrine-disrupting potential
of chemicals with limited empirical data and enable the reduction
of the use of hazardous substances.