Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications

2025/08/09 by P.P. Kobchikova, Kobchikova P. P., Bakirov B. A. +6 · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Computational Drug Discovery Methods #Machine Learning in Materials Science #Protein Structure and Dynamics

paper · doi:10.1007/s12551-025-01351-5

crossref issued 2025/08/09 · crossref published 2025/08/09 · crossref published-online 2025/08/09 · openalex publication_date 2025/08/09 · crossref created 2025/08/09 · crossref published-print 2025/10/01 · openalex created_date 2025/10/10 · crossref deposited 2026/01/27 · crossref indexed 2026/07/24 · openalex updated_date 2026/07/28

Citations

• SchNet – A deep learning architecture for molecules and materials
• Performance and Cost Assessment of Machine Learning Interatomic Potentials
• LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
• E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
• Machine Learning of Reactive Potentials
• OpenMM-Python-Force: Deploying Accelerated Python Modules in Molecular Dynamics Simulation

Cited by

• A New Insight into the Study of Neural Cell Adhesion Molecule (NCAM) Polysialylation Inhibition Incorporated the Molecular Docking Models into the NMR Spectroscopy of a Crucial Peptide–Ligand Interaction

Discussions

• 🚀 New review out in Biophysical Reviews: “Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications” 🔗 doi.org/10.1007/s125... #MLIPs #Biophysics [bsky, 3 points, 0 comments]