High-throughput quantum theory of atoms in molecules (QTAIM) for geometric deep learning of molecular and reaction properties

Vargas, Santiago; Gee, Winston; Alexandrova, Anastassia

doi:10.1039/d4dd00057a

Cited by 1 publication

(1 citation statement)

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“…Despite these advances for molecular property prediction, the prediction of computed reaction properties (principally, reaction barriers ,,− ) is still in its infancy . Machine learning approaches span from utilizing simple two-dimensional fingerprints of reaction components , (reactants and products) to physical-organic descriptors, ,,,,− or electronic structure-inspired features, to transformer models , adapted for regression, and 2D graph-based approaches. ,,, The latter, particularly the ChemProp model, , are often best-in-class in predicting reaction properties . It has been shown that these models achieve their impressive performance by exploiting atom-mapping information, − which provide information analogous to the reaction mechanism.…”

Section: Introductionmentioning

confidence: 99%

3DReact: Geometric Deep Learning for Chemical Reactions

van Gerwen,

Briling,

Bunne

et al. 2024

J. Chem. Inf. Model.

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Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DREACT, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DREACT offers a flexible framework that exploits atommapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different data sets, atom-mapping regimes, as well as both interpolation and extrapolation tasks.

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