Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Predictive modeling of antibacterial activity of ionic liquids by machine learning methods. / Makarov, D.m.; Fadeeva, Yu.a.; Сафонова, Евгения Алексеевна; Shmukler, L.e.
в: Computational Biology and Chemistry, Том 101, 107775, 01.12.2022.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
}
TY - JOUR
T1 - Predictive modeling of antibacterial activity of ionic liquids by machine learning methods
AU - Makarov, D.m.
AU - Fadeeva, Yu.a.
AU - Сафонова, Евгения Алексеевна
AU - Shmukler, L.e.
N1 - Publisher Copyright: © 2022 Elsevier Ltd
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Structural variation and different bioactivity of ionic liquids (ILs) make them highly promising for the development of novel biocides. Application of computational methods to the evaluation of potential antibacterial activity of chemical compounds is a useful, time- and cost-saving tool replacing numerous experimental syntheses. In the present study, quantitative structure–activity relationship (QSAR) modeling is applied to develop models (based on more than 800 data points) aiming to predict the minimal inhibitory concentration (MIC) of ILs against three types of human pathogens – Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa. The random forest model with the AlvaDesc descriptors in general demonstrates the best performance for all the three types of bacteria and is suggested as a final model. To interpret the final model and determine the most significant descriptors, a SHapley Additive exPlanation (SHAP) method was applied. Six amino acid ILs, which were synthesized for the first time, and five halogenide ionic liquids purchased, all based on 1-alkyl-3methylimidozolium cations with different alkyl chain lengths, C10, C12 and C14, are tested in vitro and used to validate the developed QSAR models. The data sets and developed model are available free of charge at http://ochem.eu/article/147386.
AB - Structural variation and different bioactivity of ionic liquids (ILs) make them highly promising for the development of novel biocides. Application of computational methods to the evaluation of potential antibacterial activity of chemical compounds is a useful, time- and cost-saving tool replacing numerous experimental syntheses. In the present study, quantitative structure–activity relationship (QSAR) modeling is applied to develop models (based on more than 800 data points) aiming to predict the minimal inhibitory concentration (MIC) of ILs against three types of human pathogens – Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa. The random forest model with the AlvaDesc descriptors in general demonstrates the best performance for all the three types of bacteria and is suggested as a final model. To interpret the final model and determine the most significant descriptors, a SHapley Additive exPlanation (SHAP) method was applied. Six amino acid ILs, which were synthesized for the first time, and five halogenide ionic liquids purchased, all based on 1-alkyl-3methylimidozolium cations with different alkyl chain lengths, C10, C12 and C14, are tested in vitro and used to validate the developed QSAR models. The data sets and developed model are available free of charge at http://ochem.eu/article/147386.
KW - Antibacterial activity
KW - Ionic Liquids
KW - OCHEM
KW - QSAR
UR - https://www.mendeley.com/catalogue/9da001bd-0c31-335d-a6e7-4dd1543a4cd0/
UR - http://www.scopus.com/inward/record.url?scp=85140034455&partnerID=8YFLogxK
U2 - 10.1016/j.compbiolchem.2022.107775
DO - 10.1016/j.compbiolchem.2022.107775
M3 - Article
VL - 101
JO - Computational Biology and Chemistry
JF - Computational Biology and Chemistry
SN - 1476-9271
M1 - 107775
ER -
ID: 99470163