Derisking Future Agrochemicals before They Are Made: Large-Scale In Vitro Screening for In Silico Modeling of Thyroid Peroxidase Inhibition

Inhibition of thyroid peroxidase (TPO) is a known molecular
initiating
event for thyroid hormone dysregulation and thyroid toxicity. Consequently,
TPO is a critical off-target for the design of safer agrochemicals.
To date, fewer than 500 structurally characterized TPO inhibitors
are known, and the most comprehensive result set generated under identical
conditions encompasses approximately 1000 compounds from a subset
of the ToxCast compound collection. Here we describe a collaboration
between wet lab and data scientists combining a large in vitro screen
and the subsequent development of an in silico model for predicting
TPO inhibition. The screen encompassed more than 100,000 diverse drug-like
agrochemical compounds and yielded more than 6000 structurally novel
TPO inhibitors. On this foundation, we applied different machine learning
techniques and compared their performance. We discuss use cases for
in silico TPO models in agrochemical research and explain that model
recall is of particular importance when selecting compounds from large
virtual compound collections. Furthermore, we show that due to the
higher structural diversity of our training data, our final model
allowed better generalization than models trained on the ToxCast data
set. We now have a tool to predict TPO inhibition even for molecules
that are only available virtually, such as hits from virtual screenings,
or compounds under consideration for inclusion in our screening collection.
Structures and activity data for 34,524 compounds are provided. This
data set includes almost all inhibitors, including more than 3000
proprietary structures, and a large proportion of the inactives.