Home / Publications / Machine learning-enabled prediction of ecotoxicity (EC50) of diverse organic compounds via infrared spectroscopy

Machine learning-enabled prediction of ecotoxicity (EC50) of diverse organic compounds via infrared spectroscopy

Maksim Yu Sidorov 1
Maksim Yu Sidorov
, 
Mikhail Eldarovich Gasanov 2
Mikhail Eldarovich Gasanov
, 
Artur Al'bertovich Dzeranov 1, 3
Artur Al'bertovich Dzeranov
, 
Lyubov Sergeevna Bondarenko 1
Lyubov Sergeevna Bondarenko
, 
Anastasiya Pavlovna Kiryushina 4
Anastasiya Pavlovna Kiryushina
,  , 
Gulzhian Iskakovna Dzhardimalieva 1, 3
Gulzhian Iskakovna Dzhardimalieva
, 
Kamila Asylbekovna Kydralieva 1
Kamila Asylbekovna Kydralieva
1 Moscow Aviation Institute (National Research University), Moscow, Russian Federation
3 Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka, Moscow Region, Russian Federation
4 A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, Russian Federation
5 Department of Soil Science, M.V. Lomonosov Moscow State University, Moscow, Russian Federation
10.1016/j.mencom.2024.10.004
Published 2024-10-22
Communication , Volume 34, Issue 6, 780-782
1
Share
Cite this
GOST
 | 
Cite this
GOST Copy
Sidorov M. Yu. et al. Machine learning-enabled prediction of ecotoxicity (EC50) of diverse organic compounds via infrared spectroscopy // Mendeleev Communications. 2024. Vol. 34. No. 6. pp. 780-782.
GOST all authors (up to 50) Copy
Sidorov M. Yu., Gasanov M. E., Dzeranov A. A., Bondarenko L. S., Kiryushina A. P., Terekhova V. A., Dzhardimalieva G. I., Kydralieva K. A. Machine learning-enabled prediction of ecotoxicity (EC50) of diverse organic compounds via infrared spectroscopy // Mendeleev Communications. 2024. Vol. 34. No. 6. pp. 780-782.
RIS
 | 
Cite this
RIS Copy
TY - JOUR
DO - 10.1016/j.mencom.2024.10.004
UR - https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.004
TI - Machine learning-enabled prediction of ecotoxicity (EC50) of diverse organic compounds via infrared spectroscopy
T2 - Mendeleev Communications
AU - Sidorov, Maksim Yu
AU - Gasanov, Mikhail Eldarovich
AU - Dzeranov, Artur Al'bertovich
AU - Bondarenko, Lyubov Sergeevna
AU - Kiryushina, Anastasiya Pavlovna
AU - Terekhova, Vera Aleksandrovna
AU - Dzhardimalieva, Gulzhian Iskakovna
AU - Kydralieva, Kamila Asylbekovna
PY - 2024
DA - 2024/10/22
PB - Mendeleev Communications
SP - 780-782
IS - 6
VL - 34
ER -
BibTex
 | 
Cite this
BibTex (up to 50 authors) Copy
@article{2024_Sidorov,
author = {Maksim Yu Sidorov and Mikhail Eldarovich Gasanov and Artur Al'bertovich Dzeranov and Lyubov Sergeevna Bondarenko and Anastasiya Pavlovna Kiryushina and Vera Aleksandrovna Terekhova and Gulzhian Iskakovna Dzhardimalieva and Kamila Asylbekovna Kydralieva},
title = {Machine learning-enabled prediction of ecotoxicity (EC50) of diverse organic compounds via infrared spectroscopy},
journal = {Mendeleev Communications},
year = {2024},
volume = {34},
publisher = {Mendeleev Communications},
month = {Oct},
url = {https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.004},
number = {6},
pages = {780--782},
doi = {10.1016/j.mencom.2024.10.004}
}
MLA
Cite this
MLA Copy
Sidorov, Maksim Yu., et al. “Machine learning-enabled prediction of ecotoxicity (EC50) of diverse organic compounds via infrared spectroscopy.” Mendeleev Communications, vol. 34, no. 6, Oct. 2024, pp. 780-782. https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.004.

Keywords

algae
EC50
ecotoxicology
effective concentration
feature importance
infrared spectroscopy
machine learning.

Abstract

A new, less time-consuming and resource-intensive approach to predicting the EC50 ecotoxicity index, which is crucial for assessing the impact of compounds on ecosystems, is proposed. Efficient EC50 prediction based on infrared spectroscopy data and EC50 values from the EcoTOX database is achieved using machine learning. The best results with an F1-score of 0.83 were obtained with the SVC and XGBoost models.

References

.
Fabrication, Microstructure and Colloidal Stability of Humic Acids Loaded Fe3O4/APTES Nanosorbents for Environmental Applications
Bondarenko L., Illés E., Tombácz E., Dzhardimalieva G., Golubeva N., Tushavina O., Adachi Y., Kydralieva K.
Nanomaterials, 2021
.
Machine Learning Interpretability: A Survey on Methods and Metrics
Carvalho D.V., Pereira E.M., Cardoso J.S.
Electronics (Switzerland), 2019
.
An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation
Vo A.H., Van Vleet T.R., Gupta R.R., Liguori M.J., Rao M.S.
Chemical Research in Toxicology, 2019
.
The ECOTOXicology Knowledgebase: A Curated Database of Ecologically Relevant Toxicity Tests to Support Environmental Research and Risk Assessment
Olker J.H., Elonen C.M., Pilli A., Anderson A., Kinziger B., Erickson S., Skopinski M., Pomplun A., LaLone C.A., Russom C.L., Hoff D.
Environmental Toxicology and Chemistry, 2022
.
Machine learning methods for estimation the indicators of phosphogypsum influence in soil
Pukalchik M.A., Katrutsa A.M., Shadrin D., Terekhova V.A., Oseledets I.V.
Journal of Soils and Sediments, 2019
.
k-Nearest Neighbour Classifiers - A Tutorial
Cunningham P., Delany S.J.
ACM Computing Surveys, 2021
.
A statistical design approach to sol-gel synthesis of (amino)organosilane hybrid nanoparticles
Bondarenko L., Saveliev Y., Chernyaev D., Baimuratova R., Dzhardimalieva G.I., Dzeranov A., Kelbysheva E., Kydralieva K.
Physical Chemistry Chemical Physics, 2023
.
A benchmark dataset for machine learning in ecotoxicology
Schür C., Gasser L., Perez-Cruz F., Schirmer K., Baity-Jesi M.
Scientific data, 2023