Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods

Computational
methods to predict molecular properties regarding
safety and toxicology represent alternative approaches to expedite
drug development, screen environmental chemicals, and thus significantly
reduce associated time and costs. There is a strong need and interest
in the development of computational methods that yield reliable predictions
of toxicity, and many approaches, including the recently introduced
deep neural networks, have been leveraged towards this goal. Herein,
we report on the collection, curation, and integration of data from
the public data sets that were the source of the ChemIDplus database
for systemic acute toxicity. These efforts generated the largest publicly
available such data set comprising > 80,000 compounds measured
against
a total of 59 acute systemic toxicity end points. This data was used
for developing multiple single- and multitask models utilizing random
forest, deep neural networks, convolutional, and graph convolutional
neural network approaches. For the first time, we also reported the
consensus models based on different multitask approaches. To the best
of our knowledge, prediction models for 36 of the 59 end points have
never been published before. Furthermore, our results demonstrated
a significantly better performance of the consensus model obtained
from three multitask learning approaches that particularly predicted
the 29 smaller tasks (less than 300 compounds) better than other models
developed in the study. The curated data set and the developed models
have been made publicly available at https://github.com/ncats/ld50-multitask, https://predictor.ncats.io/, and https://cactus.nci.nih.gov/download/acute-toxicity-db (data
set only) to support regulatory and research applications.