Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.

There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) and computational methods are vital. As part of one...

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Autores principales:	Adrian J Green, Martin J Mohlenkamp, Jhuma Das, Meenal Chaudhari, Lisa Truong, Robyn L Tanguay, David M Reif
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/5275e5c7bd414dfaa9a5ebbc012a0e38
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spelling	oai:doaj.org-article:5275e5c7bd414dfaa9a5ebbc012a0e382021-12-02T19:57:34ZLeveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.1553-734X1553-735810.1371/journal.pcbi.1009135https://doaj.org/article/5275e5c7bd414dfaa9a5ebbc012a0e382021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009135https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) and computational methods are vital. As part of one such HTS effort, embryonic zebrafish were used to examine a suite of morphological and mortality endpoints at six concentrations from over 1,000 unique chemicals found in the ToxCast library (phase 1 and 2). We hypothesized that by using a conditional generative adversarial network (cGAN) or deep neural networks (DNN), and leveraging this large set of toxicity data we could efficiently predict toxic outcomes of untested chemicals. Utilizing a novel method in this space, we converted the 3D structural information into a weighted set of points while retaining all information about the structure. In vivo toxicity and chemical data were used to train two neural network generators. The first was a DNN (Go-ZT) while the second utilized cGAN architecture (GAN-ZT) to train generators to produce toxicity data. Our results showed that Go-ZT significantly outperformed the cGAN, support vector machine, random forest and multilayer perceptron models in cross-validation, and when tested against an external test dataset. By combining both Go-ZT and GAN-ZT, our consensus model improved the SE, SP, PPV, and Kappa, to 71.4%, 95.9%, 71.4% and 0.673, respectively, resulting in an area under the receiver operating characteristic (AUROC) of 0.837. Considering their potential use as prescreening tools, these models could provide in vivo toxicity predictions and insight into the hundreds of thousands of untested chemicals to prioritize compounds for HT testing.Adrian J GreenMartin J MohlenkampJhuma DasMeenal ChaudhariLisa TruongRobyn L TanguayDavid M ReifPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009135 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Adrian J Green Martin J Mohlenkamp Jhuma Das Meenal Chaudhari Lisa Truong Robyn L Tanguay David M Reif Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.
description	There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) and computational methods are vital. As part of one such HTS effort, embryonic zebrafish were used to examine a suite of morphological and mortality endpoints at six concentrations from over 1,000 unique chemicals found in the ToxCast library (phase 1 and 2). We hypothesized that by using a conditional generative adversarial network (cGAN) or deep neural networks (DNN), and leveraging this large set of toxicity data we could efficiently predict toxic outcomes of untested chemicals. Utilizing a novel method in this space, we converted the 3D structural information into a weighted set of points while retaining all information about the structure. In vivo toxicity and chemical data were used to train two neural network generators. The first was a DNN (Go-ZT) while the second utilized cGAN architecture (GAN-ZT) to train generators to produce toxicity data. Our results showed that Go-ZT significantly outperformed the cGAN, support vector machine, random forest and multilayer perceptron models in cross-validation, and when tested against an external test dataset. By combining both Go-ZT and GAN-ZT, our consensus model improved the SE, SP, PPV, and Kappa, to 71.4%, 95.9%, 71.4% and 0.673, respectively, resulting in an area under the receiver operating characteristic (AUROC) of 0.837. Considering their potential use as prescreening tools, these models could provide in vivo toxicity predictions and insight into the hundreds of thousands of untested chemicals to prioritize compounds for HT testing.
format	article
author	Adrian J Green Martin J Mohlenkamp Jhuma Das Meenal Chaudhari Lisa Truong Robyn L Tanguay David M Reif
author_facet	Adrian J Green Martin J Mohlenkamp Jhuma Das Meenal Chaudhari Lisa Truong Robyn L Tanguay David M Reif
author_sort	Adrian J Green
title	Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.
title_short	Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.
title_full	Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.
title_fullStr	Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.
title_full_unstemmed	Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.
title_sort	leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/5275e5c7bd414dfaa9a5ebbc012a0e38
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Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.

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