EquiReact: An equivariant neural network for chemical reactions
Puck van Gerwen Ksenia R. Briling Charlotte Bunne Vignesh Ram Somnath Ruben Laplaza Andreas Krause Clemence Corminboeuf
Equivariant neural networks have considerably improved the accuracy and
data-efficiency of predictions of molecular properties. Building on this
success, we introduce EquiReact, an equivariant neural network to infer
properties of chemical reactions, built from three-dimensional structures of
reactants and products. We illustrate its competitive performance on the
prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS and
Proparg-21-TS datasets with different regimes according to the inclusion of
atom-mapping information. We show that, compared to state-of-the-art models for
reaction property prediction, EquiReact offers: (i) a flexible model with
reduced sensitivity between atom-mapping regimes, (ii) better extrapolation
capabilities to unseen chemistries, (iii) impressive prediction errors for
datasets exhibiting subtle variations in three-dimensional geometries of
reactants/products, (iv) reduced sensitivity to geometry quality and (iv)
excellent data efficiency.