aAlexander Butlerov Institute of Chemistry, Kazan Federal University, Kazan, Russian Federation bInstitute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo, Japan cDepartment of Materials Science and Engineering, Technion – Israel Institute of Technology, Haifa, Israel dLaboratoire de Modelisation et Simulations Moleculaires, Universite Louis Pasteur, Strasbourg, France
Аннотация:
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.
Образец цитирования:
T. I. Madzhidov, A. Rakhimbekova, V. A. Afonina, T. R. Gimadiev, R. N. Mukhametgaliev, R. I. Nugmanov, I. I. Baskin, A. Varnek, “Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow”, Mendeleev Commun., 31:6 (2021), 769–780
Образцы ссылок на эту страницу:
https://www.mathnet.ru/rus/mendc1040
https://www.mathnet.ru/rus/mendc/v31/i6/p769
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