東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Machine Learning Methods for Advancing Computational Chemistry.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Methods for Advancing Computational Chemistry./
作者:	Gupta, Aayush.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	138 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
標題:	Physical chemistry. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29330601
ISBN:	9798834054542

Machine Learning Methods for Advancing Computational Chemistry.
Gupta, Aayush.

Machine Learning Methods for Advancing Computational Chemistry. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 138 p.

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--University of Illinois at Chicago, 2022.

This item must not be sold to any third party vendors.

Computational Chemistry offers accurate physics-based simulation methods to assist in drug discovery and protein modeling problems. The associated computational costs grow significantly with a number of atoms. Machine learning algorithms solve this problem by accelerating computations while preserving accuracy. Recently, a deep neural network has obtained nearly exact solutions of the electronic Schrodinger equation for small molecules. Another recent breakthrough is the prediction of three-dimensional structures of proteins by neural network-based methods, Alphafold and RoseTTafold.In my PhD, I worked on three different domains of computational chemistry where we integrated machine learning to improve as well as accelerate conventional techniques; 1) Designed an efficient workflow for drug discovery by integrating a hybrid neural-network (pseudo-quantum) and classical forcefields based molecular dynamics simulations against COVID-19 main protease (MPRO), 2) Developed a machine learning- enabled pipeline for large-scale virtual drug screening using clustering and deep learning in combination with physics-based approaches against RPN11 - a drug target for breast cancer, and 3) Implemented a generative deep network to guide conformational sampling of intrinsically disordered proteins; an efficient protocol to accelerate molecular dynamics simulations. Together, these PhD projects present a new perspective on how machine learning can be used in the rapid and accurate screening of new drugs and accelerating molecular dynamics simulations for mining proteins conformational landscape. With the advances in the machine learning field, this work will have many other possible applications to help accelerate molecular dynamics and drug-discovery platforms.

ISBN: 9798834054542Subjects--Topical Terms:

1981412
Physical chemistry.
Subjects--Index Terms:

Computational chemistry

Machine Learning Methods for Advancing Computational Chemistry.
LDR:03057nmm a2200421 4500 001 2352284
005 20221118093905.5
008 241004s2022 ||||||||||||||||| ||eng d
020 $a 9798834054542
035 $a (MiAaPQ)AAI29330601
035 $a (MiAaPQ)0799vireo3005Gupta
035 $a AAI29330601
040 $a MiAaPQ $c MiAaPQ
100 1 $a Gupta, Aayush. $3 3283935
245 1 0 $a Machine Learning Methods for Advancing Computational Chemistry.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 138 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
500 $a Advisor: Zhou, Huan-Xiang.
502 $a Thesis (Ph.D.)--University of Illinois at Chicago, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Computational Chemistry offers accurate physics-based simulation methods to assist in drug discovery and protein modeling problems. The associated computational costs grow significantly with a number of atoms. Machine learning algorithms solve this problem by accelerating computations while preserving accuracy. Recently, a deep neural network has obtained nearly exact solutions of the electronic Schrodinger equation for small molecules. Another recent breakthrough is the prediction of three-dimensional structures of proteins by neural network-based methods, Alphafold and RoseTTafold.In my PhD, I worked on three different domains of computational chemistry where we integrated machine learning to improve as well as accelerate conventional techniques; 1) Designed an efficient workflow for drug discovery by integrating a hybrid neural-network (pseudo-quantum) and classical forcefields based molecular dynamics simulations against COVID-19 main protease (MPRO), 2) Developed a machine learning- enabled pipeline for large-scale virtual drug screening using clustering and deep learning in combination with physics-based approaches against RPN11 - a drug target for breast cancer, and 3) Implemented a generative deep network to guide conformational sampling of intrinsically disordered proteins; an efficient protocol to accelerate molecular dynamics simulations. Together, these PhD projects present a new perspective on how machine learning can be used in the rapid and accurate screening of new drugs and accelerating molecular dynamics simulations for mining proteins conformational landscape. With the advances in the machine learning field, this work will have many other possible applications to help accelerate molecular dynamics and drug-discovery platforms.
590 $a School code: 0799.
650 4 $a Physical chemistry. $3 1981412
650 4 $a Computer science. $3 523869
650 4 $a Computational chemistry. $3 3350019
650 4 $a Pharmaceutical sciences. $3 3173021
650 4 $a Epidemiology. $3 568544
653 $a Computational chemistry
653 $a Machine Learning
653 $a COVID-19
653 $a Intrinsically Disordered Proteins
653 $a Drug discovery
690 $a 0494
690 $a 0984
690 $a 0800
690 $a 0219
690 $a 0766
690 $a 0572
710 2 $a University of Illinois at Chicago. $b Chemistry. $3 2096825
773 0 $t Dissertations Abstracts International $g 84-02B.
790 $a 0799
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29330601