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Machine Learning Models in Drug Disc...

Gupta, Rohan.

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Machine Learning Models in Drug Discovery and Development.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Models in Drug Discovery and Development./
Author:	Gupta, Rohan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	70 p.
Notes:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
Subject:	Pharmacology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30567546
ISBN:	9798380377164

Machine Learning Models in Drug Discovery and Development.
Gupta, Rohan.

Machine Learning Models in Drug Discovery and Development. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 70 p.

Source: Masters Abstracts International, Volume: 85-03.

Thesis (M.S.)--Drexel University, 2023.

Machine learning models have seen increased use in drug discovery and development. With the rise in their popularity, it has become important to describe machine learning in a manner that is easily understandable to a researcher not well-versed in machine learning. This thesis introduces machine learning to such researchers, with particular focus on supervised machine learning models that learn to make predictions about a target concept from training data. A general overview of the machine learning methods and the molecular data representations are provided, and two case studies are discussed to demonstrate some of the common applications: drug-target interaction prediction using artificial neural networks, and drug property prediction using decision trees.

ISBN: 9798380377164Subjects--Topical Terms:

634543
Pharmacology.
Subjects--Index Terms:

Decision trees

Machine Learning Models in Drug Discovery and Development.
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245 1 0 $a Machine Learning Models in Drug Discovery and Development.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 70 p.
500 $a Source: Masters Abstracts International, Volume: 85-03.
500 $a Advisor: Sacan, Ahmet;McGonigle, Paul.
502 $a Thesis (M.S.)--Drexel University, 2023.
520 $a Machine learning models have seen increased use in drug discovery and development. With the rise in their popularity, it has become important to describe machine learning in a manner that is easily understandable to a researcher not well-versed in machine learning. This thesis introduces machine learning to such researchers, with particular focus on supervised machine learning models that learn to make predictions about a target concept from training data. A general overview of the machine learning methods and the molecular data representations are provided, and two case studies are discussed to demonstrate some of the common applications: drug-target interaction prediction using artificial neural networks, and drug property prediction using decision trees.
590 $a School code: 0065.
650 4 $a Pharmacology. $3 634543
653 $a Decision trees
653 $a Drug development
653 $a Drug discovery
653 $a Machine learning
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690 $a 0800
710 2 $a Drexel University. $b Pharmacology and Physiology (College of Medicine). $3 3771154
773 0 $t Masters Abstracts International $g 85-03.
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792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30567546