| Autor: |
Zankov, Dmitry, Madzhidov, Timur, Baskin, Igor, Varnek, Alexandre |
| Předmět: |
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| Zdroj: |
Molecular Informatics; Oct2023, Vol. 42 Issue 10, p1-10, 10p |
| Abstrakt: |
Conjugated QSPR models for reactions integrate fundamental chemical laws expressed by mathematical equations with machine learning algorithms. Herein we present a methodology for building conjugated QSPR models integrated with the Arrhenius equation. Conjugated QSPR models were used to predict kinetic characteristics of cycloaddition reactions related by the Arrhenius equation: rate constant logk ${{\rm l}{\rm o}{\rm g}k}$ , pre‐exponential factor logA ${{\rm l}{\rm o}{\rm g}A}$ , and activation energy Ea ${{E}_{{\rm a}}}$. They were benchmarked against single‐task (individual and equation‐based models) and multi‐task models. In individual models, all characteristics were modeled separately, while in multi‐task models logk ${{\rm l}{\rm o}{\rm g}k}$ , logA ${{\rm l}{\rm o}{\rm g}A}$ and Ea ${{E}_{{\rm a}}}$ were treated cooperatively. An equation‐based model assessed logk ${{\rm l}{\rm o}{\rm g}k}$ using the Arrhenius equation and logA ${{\rm l}{\rm o}{\rm g}A}$ and Ea ${{E}_{{\rm a}}}$ values predicted by individual models. It has been demonstrated that the conjugated QSPR models can accurately predict the reaction rate constants at extreme temperatures, at which reaction rate constants hardly can be measured experimentally. Also, in the case of small training sets conjugated models are more robust than related single‐task approaches. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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