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Machine Learning Estimation of Atom Condensed Fukui Functions.

Qingyou Zhang1, Fangfang Zheng1, Tanfeng Zhao1

  • 1Institute of Environmental and Analytical Sciences, College of Chemistry and Chemical Engineering, Henan University, Kaifeng, 475004, PR China.

Molecular Informatics
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Summary

Machine learning models rapidly estimate atom condensed Fukui functions for organic molecules. Random Forests accurately predict Fukui functions and rank atoms, aiding chemical reactivity predictions.

Keywords:
Bradley-Terry ModelsChemoinformaticsQSPRQuantum ChemistryRandom Forest

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Area of Science:

  • Computational chemistry
  • Machine learning in chemistry
  • Quantum chemistry

Background:

  • The Fukui function is crucial for predicting nucleophilic and electrophilic reactivity in molecules.
  • Accurate estimation of condensed Fukui functions is computationally intensive using traditional methods like Density Functional Theory (DFT).
  • Developing faster methods for Fukui function estimation is essential for large-scale chemical analysis.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for the rapid estimation of atom condensed Fukui functions.
  • To compare the performance of different ML approaches, including the Bradley-Terry (BT) model and Random Forests (RF), for Fukui function prediction and atom ranking.
  • To establish ML-driven methods for identifying atoms with high Fukui functions in organic molecules.

Main Methods:

  • Trained ML algorithms using a database of DFT-calculated condensed Fukui functions for approximately 23,000 atoms in organic molecules.
  • Employed the Bradley-Terry (BT) model for ranking atom types and Random Forests (RF) for regression and classification tasks.
  • Utilized atomic descriptors based on the counts of atom types within defined spherical regions around a central atom.

Main Results:

  • The BT model achieved 93-94% accuracy in identifying the atom with the highest Fukui function in atom pairs with significant differences.
  • RF regression models yielded predictive performance with R-squared values of 0.68-0.69, outperforming BT coefficients in ranking atoms within entire molecules.
  • RF classification models demonstrated high specificity (94-95%) for classifying atoms as having high or low Fukui functions.

Conclusions:

  • Machine learning, particularly Random Forests, offers a computationally efficient approach for estimating atom condensed Fukui functions.
  • ML models can effectively rank atoms by their Fukui functions, aiding in the prediction of chemical reactivity.
  • These ML methods provide a valuable tool for accelerating chemical research and discovery by enabling fast Fukui function estimations.