Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins

2024/01/01 by Kai Riedmiller, Patrick Reiser, Elizaveta Bobkova +4 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Computational Drug Discovery Methods #Machine Learning in Materials Science #Protein Structure and Dynamics

paper · pdf · doi:10.1039/d3sc03922f

openalex publication_date 2024/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03

Abstract

). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.

Citations

• A general method for numerically simulating the stochastic time evolution of coupled chemical reactions
• DScribe: Library of descriptors for machine learning in materials science
• E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
• Development of density functionals for thermochemical kinetics
• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• Equivariant message passing for the prediction of tensorial properties and molecular spectra

Discussions

• Predict reaction barriers by GNNs for complex and dynamic systems in seconds. So far for hydrogen atom transfer but more to come. @ChemicalScience https://doi.org/10.1039/d3sc03922f Thanks to Kai and [bsky, 1 points, 0 comments]

Related

• Dynamics of biochemical and biophysical reactions: insight from computer simulations
• Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides
• A kinetic perspective of charge transfer reactions: the downfall of hard/soft acid/base interactions
• Hydrogen bond models for the simulation of protein folding and aggregation
• Medienpolitik: Gepflegtes Desinteresse