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Computational Approaches for the Des...

Shebek, Kevin.

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Computational Approaches for the Design and Screening of Novel Biopolymers.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational Approaches for the Design and Screening of Novel Biopolymers./
Author:	Shebek, Kevin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	127 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Chemical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30995546
ISBN:	9798382759081

Computational Approaches for the Design and Screening of Novel Biopolymers.
Shebek, Kevin.

Computational Approaches for the Design and Screening of Novel Biopolymers. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 127 p.

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--Northwestern University, 2024.

Reliance on polymers produced from traditional fossil fuel feedstocks has led to an increase in greenhouse gas emissions and long-lived waste in the environment. Biopolymers offer an attractive solution to this problem owing to their sustainable production, biocompatibility, biodegradability, and tunability. However, the design space of these polymers is vast and difficult to explore. In this dissertation, a high-throughput computational screening method is used to design novel biopolymers and pathways for their synthesis. The first portion of the thesis develops computational tools necessary to create and analyze biopolymers. First, Pickaxe, an automated reaction network generation tool, is developed. This tool utilizes reaction rules to generate synthesis pathways from a set of starting compounds into a novel set of compounds. The next section of the thesis details the development of PolyIDRS, a machine learning prediction tool for polymer properties. PolyIDRS is the first tool of its kind to utilize stereochemistry in polymers, which is ubiquitous in biopolymers and is essential for the design of novel materials. In the final portion of this dissertation, these tools are combined to produce a computational workflow to identify novel polyhydroxyalkanoates, which are an attractive biopolymer target due to their tunability and biodegradability. Combining Pickaxe and PolyIDRS allows for the generation of novel biopolymers to replace traditional polymers as well as the design of more sustainable synthesis pathways for existing polymers. Collectively, this dissertation demonstrates the utility of combining computational tools to generate high-throughput screening methods to discover the next generation of biopolymers.

ISBN: 9798382759081Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Machine learning

Computational Approaches for the Design and Screening of Novel Biopolymers.
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245 1 0 $a Computational Approaches for the Design and Screening of Novel Biopolymers.
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300 $a 127 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisor: Broadbelt, Linda J.;Tyo, Keith E. J.
502 $a Thesis (Ph.D.)--Northwestern University, 2024.
520 $a Reliance on polymers produced from traditional fossil fuel feedstocks has led to an increase in greenhouse gas emissions and long-lived waste in the environment. Biopolymers offer an attractive solution to this problem owing to their sustainable production, biocompatibility, biodegradability, and tunability. However, the design space of these polymers is vast and difficult to explore. In this dissertation, a high-throughput computational screening method is used to design novel biopolymers and pathways for their synthesis. The first portion of the thesis develops computational tools necessary to create and analyze biopolymers. First, Pickaxe, an automated reaction network generation tool, is developed. This tool utilizes reaction rules to generate synthesis pathways from a set of starting compounds into a novel set of compounds. The next section of the thesis details the development of PolyIDRS, a machine learning prediction tool for polymer properties. PolyIDRS is the first tool of its kind to utilize stereochemistry in polymers, which is ubiquitous in biopolymers and is essential for the design of novel materials. In the final portion of this dissertation, these tools are combined to produce a computational workflow to identify novel polyhydroxyalkanoates, which are an attractive biopolymer target due to their tunability and biodegradability. Combining Pickaxe and PolyIDRS allows for the generation of novel biopolymers to replace traditional polymers as well as the design of more sustainable synthesis pathways for existing polymers. Collectively, this dissertation demonstrates the utility of combining computational tools to generate high-throughput screening methods to discover the next generation of biopolymers.
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