Home / Publications / Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow

Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow

, 
Assima Rakhimbekova 1
Assima Rakhimbekova
, 
Valentina Aleksandrovna Afonina 1
Valentina Aleksandrovna Afonina
, 
Timur R Gimadiev 2
Timur R Gimadiev
, 
Ravil Nailevich Mukhametgaliev 1
Ravil Nailevich Mukhametgaliev
, 
Ramil Irekovich Nugmanov 1
Ramil Irekovich Nugmanov
,  , 
Alexandre Alekseevich Varnek 2, 4
Alexandre Alekseevich Varnek
10.1016/j.mencom.2021.11.003
Published 2021-11-08
Focus article , Volume 31, Issue 6, 769-780
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Madzhidov T. I. et al. Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow // Mendeleev Communications. 2021. Vol. 31. No. 6. pp. 769-780.
GOST all authors (up to 50) Copy
Madzhidov T. I., Rakhimbekova A., Afonina V. A., Gimadiev T. R., Mukhametgaliev R. N., Nugmanov R. I., Baskin I. I., Varnek A. A. Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow // Mendeleev Communications. 2021. Vol. 31. No. 6. pp. 769-780.
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TY - JOUR
DO - 10.1016/j.mencom.2021.11.003
UR - https://mendcomm.colab.ws/publications/10.1016/j.mencom.2021.11.003
TI - Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow
T2 - Mendeleev Communications
AU - Madzhidov, Timur Ismailovich
AU - Rakhimbekova, Assima
AU - Afonina, Valentina Aleksandrovna
AU - Gimadiev, Timur R
AU - Mukhametgaliev, Ravil Nailevich
AU - Nugmanov, Ramil Irekovich
AU - Baskin, Igor Iosifovich
AU - Varnek, Alexandre Alekseevich
PY - 2021
DA - 2021/11/08
PB - Mendeleev Communications
SP - 769-780
IS - 6
VL - 31
ER -
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@article{2021_Madzhidov,
author = {Timur Ismailovich Madzhidov and Assima Rakhimbekova and Valentina Aleksandrovna Afonina and Timur R Gimadiev and Ravil Nailevich Mukhametgaliev and Ramil Irekovich Nugmanov and Igor Iosifovich Baskin and Alexandre Alekseevich Varnek},
title = {Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow},
journal = {Mendeleev Communications},
year = {2021},
volume = {31},
publisher = {Mendeleev Communications},
month = {Nov},
url = {https://mendcomm.colab.ws/publications/10.1016/j.mencom.2021.11.003},
number = {6},
pages = {769--780},
doi = {10.1016/j.mencom.2021.11.003}
}
MLA
Cite this
MLA Copy
Madzhidov, Timur Ismailovich, et al. “Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow.” Mendeleev Communications, vol. 31, no. 6, Nov. 2021, pp. 769-780. https://mendcomm.colab.ws/publications/10.1016/j.mencom.2021.11.003.
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Keywords

chemical reaction
chemoinformatics
QSAR
QSPR
QSRR
reaction conditions
reaction informatics
reaction rate
reaction yield

Abstract

The synthesis of the desired chemical compound is the main task of synthetic organic chemistry. The predictions of reaction conditions and some important quantitative characteristics of chemical reactions as yield and reaction rate can substantially help in the development of optimal synthetic routes and assessment of synthesis cost. Theoretical assessment of these parameters can be performed with the help of modern machine-learning approaches, which use available experimental data to develop predictive models called quantitative or qualitative structure–reactivity relationship (QSRR) modelling. In the article, we review the state-of-the-art in the QSRR area and give our opinion on emerging trends in this field.

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