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In Silico Toxicology: Application of...

Daghighi, Amirreza.

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In Silico Toxicology: Application of Machine Learning for Predicting Toxicity of Organic Compounds.

Record Type:	Electronic resources : Monograph/item
Title/Author:	In Silico Toxicology: Application of Machine Learning for Predicting Toxicity of Organic Compounds./
Author:	Daghighi, Amirreza.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	70 p.
Notes:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
Subject:	Biomedical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30419097
ISBN:	9798379698607

In Silico Toxicology: Application of Machine Learning for Predicting Toxicity of Organic Compounds.
Daghighi, Amirreza.

In Silico Toxicology: Application of Machine Learning for Predicting Toxicity of Organic Compounds. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 70 p.

Source: Masters Abstracts International, Volume: 84-12.

Thesis (M.S.)--North Dakota State University, 2023.

Understanding the toxicity of organic compounds is essential to protect human health, the environment, and ensure the safe use of chemicals. While experimental approaches are time-consuming and costly, computational studies offer cost-effective and time-efficient to predict the toxicity of organic compounds. Moreover, computational studies can reduce the need for animal testing and provide insights into the underlying mechanisms of toxicity. This thesis aims to develop Quantitative Structure-Toxicity Relationship (QSTR) models using different Machine Learning (ML) methods to predict the toxicity of organic compounds. The first study uses ensemble learning and Support Vector Regression (SVR) to estimate the toxicity of nitroaromatic compounds. The second study employs one of the largest available toxicology datasets to build a QSTR model that predicts the toxicity of various organic compounds under different experimental conditions. The proposed computational workflow can be an important milestone in developing QSTR models and paves the way for future toxicology studies.

ISBN: 9798379698607Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Machine learning

In Silico Toxicology: Application of Machine Learning for Predicting Toxicity of Organic Compounds.
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520 $a Understanding the toxicity of organic compounds is essential to protect human health, the environment, and ensure the safe use of chemicals. While experimental approaches are time-consuming and costly, computational studies offer cost-effective and time-efficient to predict the toxicity of organic compounds. Moreover, computational studies can reduce the need for animal testing and provide insights into the underlying mechanisms of toxicity. This thesis aims to develop Quantitative Structure-Toxicity Relationship (QSTR) models using different Machine Learning (ML) methods to predict the toxicity of organic compounds. The first study uses ensemble learning and Support Vector Regression (SVR) to estimate the toxicity of nitroaromatic compounds. The second study employs one of the largest available toxicology datasets to build a QSTR model that predicts the toxicity of various organic compounds under different experimental conditions. The proposed computational workflow can be an important milestone in developing QSTR models and paves the way for future toxicology studies.
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653 $a Organic compounds
653 $a QSAR
653 $a Toxicity
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