國立虎尾科技大學 |

Machine Learning Models in Drug Discovery and Development.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Machine Learning Models in Drug Discovery and Development./
作者:	Gupta, Rohan.
面頁冊數:	1 online resource (70 pages)
附註:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
標題:	Pharmacology. -
電子資源:	click for full text (PQDT)
ISBN:	9798380377164

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

Machine Learning Models in Drug Discovery and Development. - 1 online resource (70 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380377164Subjects--Topical Terms:

583819
Pharmacology.
Subjects--Index Terms:

Decision treesIndex Terms--Genre/Form:

554714
Electronic books.

Machine Learning Models in Drug Discovery and Development.
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504 $a Includes bibliographical references
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.
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538 $a Mode of access: World Wide Web
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653 $a Decision trees
653 $a Drug development
653 $a Drug discovery
653 $a Machine learning
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