Predicting Antimicrobial Activity of Conjugated Oligoelectrolyte Molecules via Machine Learning

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Predicting Antimicrobial Activity of Conjugated Oligoelectrolyte Molecules via Machine Learning
Title:
Predicting Antimicrobial Activity of Conjugated Oligoelectrolyte Molecules via Machine Learning
Other Titles:
Journal of the American Chemical Society
Publication Date:
05 November 2021
Citation:
Tiihonen, A., Cox-Vazquez, S. J., Liang, Q., Ragab, M., Ren, Z., Hartono, N. T. P., … Buonassisi, T. (2021). Predicting Antimicrobial Activity of Conjugated Oligoelectrolyte Molecules via Machine Learning. Journal of the American Chemical Society, 143(45), 18917–18931. doi:10.1021/jacs.1c05055
Abstract:
New antibiotics are needed to battle growing antibiotic resistance, but the development process from hit, to lead, and ultimately to a useful drug takes decades. Although progress in molecular property prediction using machine-learning methods has opened up new pathways for aiding the antibiotics development process, many existing solutions rely on large data sets and finding structural similarities to existing antibiotics. Challenges remain in modeling unconventional antibiotic classes that are drawing increasing research attention. In response, we developed an antimicrobial activity prediction model for conjugated oligoelectrolyte molecules, a new class of antibiotics that lacks extensive prior structure-activity relationship studies. Our approach enables us to predict the minimum inhibitory concentration for E. coli K12, with 21 molecular descriptors selected by recursive elimination from a set of 5305 descriptors. This predictive model achieves an R2 of 0.65 with no prior knowledge of the underlying mechanism. We find the molecular representation optimum for the domain is the key to good predictions of antimicrobial activity. In the case of conjugated oligoelectrolytes, a representation reflecting the three-dimensional shape of the molecules is most critical. Although it is demonstrated with a specific example of conjugated oligoelectrolytes, our proposed approach for creating the predictive model can be readily adapted to other novel antibiotic candidate domains.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Accelerated Materials Development for Manufacturing Program - AME Programmatic Fund
Grant Reference no. : A1898b0043

This research / project is supported by the National Research Foundation - Low Energy Electronic Systems Research Program, SMART, CREATE
Grant Reference no. : Not Available (I checked with the co-author who acknowledged this grant)

This research / project is supported by the National University Singapore (NUS) - Startup Grant
Grant Reference no. : R-143-000-A97-133

i) UCSB - Institute for Collaborative Biotechnologies through grant, W911NF-09-D-0001 ii) DARPA - HR001118C0036 iii) US National Science Foundation grant CBET-1605547
Description:
This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of the American Chemical Society, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/jacs.1c05055
ISSN:
0002-7863
1520-5126
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