Authors: Dmitry Zankov | Timur Madzhidov | Igor I. Baskin | Alexandre Varnek
Source: Molecular Informatics (article, 2023-07-25)
Field: Materials Science — Materials Chemistry

Conjugated quantitative structure‐property relationship models: Prediction of kinetic characteristics linked by the Arrhenius equation 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>l</mml:mi> <mml:mi>o</mml:mi> <mml:mi>g</mml:mi> <mml:mi>k</mml:mi></mml:mrow> <mml:annotation>${{\rm l}{\rm o}{\rm g}k}$</mml:annotation> </mml:semantics> </mml:math> , pre-exponential factor <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>l</mml:mi> <mml:mi>o</mml:mi> <mml:mi>g</mml:mi> <mml:mi>A</mml:mi></mml:mrow> <mml:annotation>${{\rm l}{\rm o}{\rm g}A}$</mml:annotation> </mml:semantics> </mml:math> , and activation energy <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>E</mml:mi> <mml:mi>a</mml:mi></mml:msub> <mml:annotation>${{E}_{{\rm a}}}$</mml:annotation> </mml:semantics> </mml:math> . 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>l</mml:mi> <mml:mi>o</mml:mi> <mml:mi>g</mml:mi> <mml:mi>k</mml:mi></mml:mrow> <mml:annotation>${{\rm l}{\rm o}{\rm g}k}$</mml:annotation> </mml:semantics> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>l</mml:mi> <mml:mi>o</mml:mi> <mml:mi>g</mml:mi> <mml:mi>A</mml:mi></mml:mrow> <mml:annotation>${{\rm l}{\rm o}{\rm g}A}$</mml:annotation> </mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>E</mml:mi> <mml:mi>a</mml:mi></mml:msub> <mml:annotation>${{E}_{{\rm a}}}$</mml:annotation> </mml:semantics> </mml:math> were treated cooperatively. An equation-based model assessed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>l</mml:mi> <mml:mi>o</mml:mi> <mml:mi>g</mml:mi> <mml:mi>k</mml:mi></mml:mrow> <mml:annotation>${{\rm l}{\rm o}{\rm g}k}$</mml:annotation> </mml:semantics> </mml:math> using the Arrhenius equation and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>l</mml:mi> <mml:mi>o</mml:mi> <mml:mi>g</mml:mi> <mml:mi>A</mml:mi></mml:mrow> <mml:annotation>${{\rm l}{\rm o}{\rm g}A}$</mml:annotation> </mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>E</mml:mi> <mml:mi>a</mml:mi></mml:msub> <mml:annotation>${{E}_{{\rm a}}}$</mml:annotation> </mml:semantics> </mml:math> 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. Arrhenius equation ; Quantitative structure–activity relationship ; Reaction rate constant ; Kinetic energy ; Activation energy ; Conjugated system ; Thermodynamics ; Chemistry ; Mathematics ; Physical chemistry ; Physics ; Stereochemistry ; Kinetics ; Polymer ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering