Today there are approximately 85,000 chemicals regulated under the Toxic
Substances Control Act, with around 2,000 new chemicals introduced each year.
It is impossible to screen all of these chemicals for potential toxic effects
either via full organism in vivo studies or in vitro high-throughput screening
(HTS) programs. Toxicologists face the challenge of choosing which chemicals to
screen, and predicting the toxicity of as-yet-unscreened chemicals. Our goal is
to describe how variation in chemical structure relates to variation in
toxicological response to enable in silico toxicity characterization designed
to meet both of these challenges. With our Bayesian partially Supervised Sparse
and Smooth Factor Analysis (BS3FA) model, we learn a distance between chemicals
targeted to toxicity, rather than one based on molecular structure alone. Our
model also enables the prediction of chemical dose-response profiles based on
chemical structure (that is, without in vivo or in vitro testing) by taking
advantage of a large database of chemicals that have already been tested for
toxicity in HTS programs. We show superior simulation performance in distance
learning and modest to large gains in predictive ability compared to existing
methods. Results from the high-throughput screening data application elucidate
the relationship between chemical structure and a toxicity-relevant
high-throughput assay. An R package for BS3FA is available online at
https://github.com/kelrenmor/bs3fa