An interpretable machine learning framework for modelling macromolecular interaction mechanisms with nuclear magnetic resonance-Reference-Cited by-同舟云学术

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An interpretable machine learning framework for modelling macromolecular interaction mechanisms with nuclear magnetic resonance

Published:2023 Issue: Volume: Page:
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Stuart Samantha¹^ORCID,Watchorn Jeffrey²^ORCID,Gu Frank X.¹²³^ORCID

Affiliation:

1. Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, Ontario, M5S 3G9, Canada

2. Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario, M5S 3E5, Canada

3. Acceleration Consortium, University of Toronto, 80 St. George Street, Toronto, Ontario, M5S 3H6, Canada

Abstract

We use a glass box approach based on decision trees to understand glycoprotein binding with biomedical polymers.

Funder

Canada First Research Excellence Fund

Natural Sciences and Engineering Research Council of Canada

Canadian Federation of University Women

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00009E

Reference56 articles.

1. Innovative Methods and Applications in Mucoadhesion Research

2. Translating preclinical models to humans

3. Supervised Learning and Mass Spectrometry Predicts the in Vivo Fate of Nanomaterials

4. Proteogenomic Network Analysis of Context-Specific KRAS Signaling in Mouse-to-Human Cross-Species Translation

5. Nanomedicine toward 2040

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Investigating Molecular Interactions between Mucin and Contact Lens Thin Films Using Nuclear Magnetic Resonance;ACS Applied Polymer Materials;2024-09-13

2. Polymers for Disrupting Protein–Protein Interactions: Where Are We and Where Should We Be?;Biomacromolecules;2024-09-10

3. Transfer-based nuclear magnetic resonance uncovers unique mechanisms for protein–polymer and protein–nanoparticle binding behavior;Journal of Materials Chemistry B;2023

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