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Data-Driven Computational Design and...

Liu, Yonglan.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-Driven Computational Design and Discovery of Antifouling Materials and Amyloid Inhibitors./
Author:	Liu, Yonglan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	262 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Contained By:	Dissertations Abstracts International82-12B.
Subject:	Chemical engineering. -
Online resource:	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.
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