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ORIGINAL RESEARCH article
Front. Ind. Microbiol.
Sec. Fuels and Chemicals
Volume 2 - 2024 |
doi: 10.3389/finmi.2024.1404729
This article is part of the Research Topic Advance in Bioprocessing Design for Platform Chemicals and Biofuels Innovations View all articles
Predicting Antimicrobial Properties of Lignin Derivatives Through Combined Data Driven and Experimental Approach
Provisionally accepted- University of Kentucky, Lexington, Kentucky, United States
Meta-analysis, experimental and data-driven quantitative-structure-activity-relationship (QSAR) models were developed to predict the antimicrobial properties of lignin derivatives. Five machine learning algorithms were applied to develop QSAR models based on the ChEMBL, a public non-lignin specific database. QSAR models were refined using ordinary-least-square regressions with a meta-analysis dataset extracted from literature and an experimental dataset. The minimum inhibition concentration (MIC) values of compounds in the meta-analysis dataset correlate to classification-based descriptors and the number of aliphatic carboxylic acid groups (R 2 =0.759). Comparatively, QSARs derived from the experimental datasets suggest that the number of aromatic hydroxyl groups were better predictors of Bacterial Load Difference (BLD, R 2 =0.831) for Bacillus subtilis, while the number of alkyl aryl groups were the strongest correlation in predicting the BLD (R 2 =0.682) of Escherichia coli. This study provides insights into the type of descriptors that correlate to antimicrobial activity and guides the valorization of lignin into sustainable antimicrobials for potential applications in food preservation, fermentation, and other industrial sectors.
Keywords: Quantitative Structure-Activity Relationship, machine learning, open-source database, Meta-analysis, Lignin valorization
Received: 21 Mar 2024; Accepted: 09 Jul 2024.
Copyright: © 2024 Kalinoski, Shao and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Ryan M. Kalinoski, University of Kentucky, Lexington, 40506, Kentucky, United States
Qing Shao, University of Kentucky, Lexington, 40506, Kentucky, United States
Jian Shi, University of Kentucky, Lexington, 40506, Kentucky, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Ryan M. Kalinoski
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