Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach

Traditional experimental testing
to identify endocrine disruptors
that enhance estrogenic signaling relies on expensive and labor-intensive
experiments. We sought to design a knowledge-based deep neural network
(k-DNN) approach to reveal and organize public high-throughput screening
data for compounds with nuclear estrogen receptor α and β
(ERα and ERβ) binding potentials. The target activity
was rodent uterotrophic bioactivity driven by ERα/ERβ
activations. After training, the resultant network successfully inferred
critical relationships among ERα/ERβ target bioassays,
shown as weights of 6521 edges between 1071 neurons. The resultant
network uses an adverse outcome pathway (AOP) framework to mimic the
signaling pathway initiated by ERα and identify compounds that
mimic endogenous estrogens (i.e., estrogen mimetics). The k-DNN can
predict estrogen mimetics by activating neurons representing several
events in the ERα/ERβ signaling pathway. Therefore, this
virtual pathway model, starting from a compound’s chemistry
initiating ERα activation and ending with rodent uterotrophic
bioactivity, can efficiently and accurately prioritize new estrogen
mimetics (AUC = 0.864–0.927). This k-DNN method is a potential
universal computational toxicology strategy to utilize public high-throughput
screening data to characterize hazards and prioritize potentially
toxic compounds.