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Manuscript-Including Suppl-Material-Updated.pdf (1.62 MB)

Predicting Environmental Chemical Toxicity using a New Hybrid Deep Machine Learning Method

preprint
revised on 17.02.2021, 04:45 and posted on 17.02.2021, 09:09 by Sarita Limbu, Cyril Zakka, Sivanesan Dakshanamurthy
Humans are exposed to thousands of potentially toxic chemicals including environmental chemicals such as industrial wastes, food products, solvents, air pollutants, fertilizers, pesticides, insecticides, carcinogens, drugs, metals/metalloids, and other industrial chemicals. Approximately 300,000 such chemicals currently in use, unfortunately little is known about their potential toxicity. Determining human toxicity potential of chemicals remains a challenge due to a substantial resource required to assess a chemical in-vivo, and only a few thousand single chemicals in commercial use has been evaluated. In this study, to predict the environmental chemical toxicity, we developed a new hybrid neural network (HNN) deep learning model consisting of a Convolutional Neural Network (CNN) and multilayer perceptron (MLP) type feed forward neural network (FFNN). Our HNN deep learning model trained based on thousands of chemicals, presented the best performance for majority of the cases. Taken together, our hybrid HNN deep learning models has a wide applicability in the prediction of toxicity of any chemical category and its mixtures.

Funding

United States Department of Defense (DOD) grant CA140882

CCSG grant P30 CA051008/CA/NCI NIH HHS/United States

History

Email Address of Submitting Author

sd233@georgetown.edu

Institution

Georgetown University

Country

USA

ORCID For Submitting Author

0000-0003-3681-4121

Declaration of Conflict of Interest

None

Version Notes

Running text has been edited/updated and some figures has been moved to the supplementary materials. Both main manuscript and Supplementary materials are in single document and uploaded as single Pdf file. Thanks.

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