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Machine Learning for Small Molecule ...

Liu, Bowen.

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Machine Learning for Small Molecule Lead Optimization.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning for Small Molecule Lead Optimization./
作者:	Liu, Bowen.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	136 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
Contained By:	Dissertations Abstracts International82-05B.
標題:	Chemistry. -
電子資源:	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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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.
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