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

Data-Driven Computational Design and...

Liu, Yonglan.

FindBook

Google Book

Amazon

博客來

Data-Driven Computational Design and Discovery of Antifouling Materials and Amyloid Inhibitors.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-Driven Computational Design and Discovery of Antifouling Materials and Amyloid Inhibitors./
作者:	Liu, Yonglan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	262 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Contained By:	Dissertations Abstracts International82-12B.
標題:	Chemical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28642768
ISBN:	9798516073885

Data-Driven Computational Design and Discovery of Antifouling Materials and Amyloid Inhibitors.
Liu, Yonglan.

Data-Driven Computational Design and Discovery of Antifouling Materials and Amyloid Inhibitors. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 262 p.

Source: Dissertations Abstracts International, Volume: 82-12, Section: B.

Thesis (Ph.D.)--The University of Akron, 2021.

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

Data-driven design and study of new materials and (pre)clinical drugs have been increasing popularity with advancement of machine learning, data science, molecular simulation, and high-performance computation. To overcome the current experimental "trial-and-error" strategy for property evaluation and design of functional soft materials and pharmaceutical drugs, we developed a data-driven computational approaches for rational design and discovery antifouling materials and amyloid inhibitors.Antifouling materials and coatings have an increasing fundamental and practical applications. Specifically, we computationally studied antifouling mechanisms of (1) four different acrylamides (AMs) for their interfacial water behaviors and their interactions with a protein using all-atom, explicit solvent molecular dynamics MD simulations and (2) four poly(N-hydroxyalkyl acrylamide) (PAMs) brushes and (3) three zwitterionic brushes using a combination of molecular mechanics (MM), Monte Carlo (MC), and MD simulations. These works provide some structural insights into the design of new antifouling materials and surfaces. Beyond antifouling mechanism investigation, we developed a data-driven computational program for rational design and discovery of antifouling self-assembled monolayers (SAMs) and polymer brushes using machine learning algorithms, reveling molecular descriptors and functional groups important for antifouling properties of materials. These data-driven machine learning models can be used as an intelligent tool for determining, repurposing, and designing new superior antifouling materials and surfaces. Amyloid aggregation and formation are the common pathological characteristics of different neurodegenerative diseases involving Alzheimer disease (AD) and type II diabetes (T2D). However, currently developed amyloid inhibitors can't achieve clinic success due to their poor biocompatibility low binding affinity and selectivity, and low permeability across blood-brain barrier (BBB). In this dissertation, we proposed a drug-repurposing strategy to discover two dual amyloid inhibitors (tanshinones and genistein) against aggregates of both Aβ and hIAPP, a single amyloid inhibitor (ginnalin A) against Aβ aggregation, and a single inhibitor (cloridarol) against hIAPP aggregates and studied their inhibition mechanisms induced by specific binding using a combination of multiscale molecular simulations and experiments.The data-driven computational platforms integrate machine learning, molecular docking, Monte Carlo simulation, steered molecular dynamics, molecular dynamics, quantum mechanics, and explicit solvent model, which can overcome the conventional timescale limits, gain sufficient conformational sampling for computationally probing, and provide the concept of adverse outcome pathway to design and discover new antifouling materials and amyloid inhibitors. .

ISBN: 9798516073885Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Computational design

Data-Driven Computational Design and Discovery of Antifouling Materials and Amyloid Inhibitors.
LDR:04201nmm a2200421 4500 001 2283510
005 20211029101504.5
008 220723s2021 ||||||||||||||||| ||eng d
020 $a 9798516073885
035 $a (MiAaPQ)AAI28642768
035 $a (MiAaPQ)OhioLINKakron1619464202390549
035 $a AAI28642768
040 $a MiAaPQ $c MiAaPQ
100 1 $a Liu, Yonglan. $3 3562479
245 1 0 $a Data-Driven Computational Design and Discovery of Antifouling Materials and Amyloid Inhibitors.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 262 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
500 $a Advisor: Jie, Zheng.
502 $a Thesis (Ph.D.)--The University of Akron, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Data-driven design and study of new materials and (pre)clinical drugs have been increasing popularity with advancement of machine learning, data science, molecular simulation, and high-performance computation. To overcome the current experimental "trial-and-error" strategy for property evaluation and design of functional soft materials and pharmaceutical drugs, we developed a data-driven computational approaches for rational design and discovery antifouling materials and amyloid inhibitors.Antifouling materials and coatings have an increasing fundamental and practical applications. Specifically, we computationally studied antifouling mechanisms of (1) four different acrylamides (AMs) for their interfacial water behaviors and their interactions with a protein using all-atom, explicit solvent molecular dynamics MD simulations and (2) four poly(N-hydroxyalkyl acrylamide) (PAMs) brushes and (3) three zwitterionic brushes using a combination of molecular mechanics (MM), Monte Carlo (MC), and MD simulations. These works provide some structural insights into the design of new antifouling materials and surfaces. Beyond antifouling mechanism investigation, we developed a data-driven computational program for rational design and discovery of antifouling self-assembled monolayers (SAMs) and polymer brushes using machine learning algorithms, reveling molecular descriptors and functional groups important for antifouling properties of materials. These data-driven machine learning models can be used as an intelligent tool for determining, repurposing, and designing new superior antifouling materials and surfaces. Amyloid aggregation and formation are the common pathological characteristics of different neurodegenerative diseases involving Alzheimer disease (AD) and type II diabetes (T2D). However, currently developed amyloid inhibitors can't achieve clinic success due to their poor biocompatibility low binding affinity and selectivity, and low permeability across blood-brain barrier (BBB). In this dissertation, we proposed a drug-repurposing strategy to discover two dual amyloid inhibitors (tanshinones and genistein) against aggregates of both Aβ and hIAPP, a single amyloid inhibitor (ginnalin A) against Aβ aggregation, and a single inhibitor (cloridarol) against hIAPP aggregates and studied their inhibition mechanisms induced by specific binding using a combination of multiscale molecular simulations and experiments.The data-driven computational platforms integrate machine learning, molecular docking, Monte Carlo simulation, steered molecular dynamics, molecular dynamics, quantum mechanics, and explicit solvent model, which can overcome the conventional timescale limits, gain sufficient conformational sampling for computationally probing, and provide the concept of adverse outcome pathway to design and discover new antifouling materials and amyloid inhibitors. .
590 $a School code: 0003.
650 4 $a Chemical engineering. $3 560457
650 4 $a Polymer chemistry. $3 3173488
650 4 $a Materials science. $3 543314
650 4 $a Computer science. $3 523869
650 4 $a Pharmaceutical sciences. $3 3173021
650 4 $a Design. $3 518875
650 4 $a Neurosciences. $3 588700
653 $a Computational design
653 $a Antifouling materials
653 $a Amyloid inhibitors
653 $a Data-driven design
690 $a 0542
690 $a 0495
690 $a 0572
690 $a 0389
690 $a 0984
690 $a 0794
690 $a 0317
710 2 $a The University of Akron. $b Chemical Engineering. $3 2094126
773 0 $t Dissertations Abstracts International $g 82-12B.
790 $a 0003
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28642768