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Machine Learning for Small Molecule ...

Liu, Bowen.

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Machine Learning for Small Molecule Lead Optimization.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning for Small Molecule Lead Optimization./
Author:	Liu, Bowen.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	136 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
Contained By:	Dissertations Abstracts International82-05B.
Subject:	Chemistry. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28241800
ISBN:	9798684640896

Machine Learning for Small Molecule Lead Optimization.
Liu, Bowen.

Machine Learning for Small Molecule Lead Optimization. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 136 p.

Source: Dissertations Abstracts International, Volume: 82-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2020.

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

The development of small molecule drugs is a lengthy and expensive process that could potentially be improved by new technologies. Lead optimization is an important part of small molecule drug discovery, where initial hit molecules are gradually developed into suitable drug candidates. It can be described as an iterative cycle of design, make and test phases, which can be further broken down into a series of concrete sub-problems, namely: molecular property prediction, molecule generation, chemical synthesis planning, experimental chemical synthesis, and experimental testing. This thesis explores machine learning methods to tackle a few of the sub-problems in small molecule lead optimization, with a focus on the early design phases.

ISBN: 9798684640896Subjects--Topical Terms:

516420
Chemistry.
Subjects--Index Terms:

Small molecule drug discovery

Machine Learning for Small Molecule Lead Optimization.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 136 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
500 $a Advisor: Leskovec, Jurij;Kim, Peter;Pande, Vijay; Wender, Paul A.
502 $a Thesis (Ph.D.)--Stanford University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a The development of small molecule drugs is a lengthy and expensive process that could potentially be improved by new technologies. Lead optimization is an important part of small molecule drug discovery, where initial hit molecules are gradually developed into suitable drug candidates. It can be described as an iterative cycle of design, make and test phases, which can be further broken down into a series of concrete sub-problems, namely: molecular property prediction, molecule generation, chemical synthesis planning, experimental chemical synthesis, and experimental testing. This thesis explores machine learning methods to tackle a few of the sub-problems in small molecule lead optimization, with a focus on the early design phases.
590 $a School code: 0212.
650 4 $a Chemistry. $3 516420
650 4 $a Artificial intelligence. $3 516317
650 4 $a Computational chemistry. $3 3350019
653 $a Small molecule drug discovery
653 $a Molecular property prediction
653 $a Molecule generation
653 $a Chemical synthesis planning
690 $a 0800
690 $a 0219
690 $a 0485
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 82-05B.
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791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28241800