Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

• Published in: Journal of chemical theory and computation 2024-01, Vol.20 (1), p.411-420
• Main Authors: Coste, Amaury, Slejko, Ema, Zavadlav, Julija, Praprotnik, Matej
• Format: Article
• Language: English
• Subjects: Biomolecular Systems; Biomolecules; Environment models; Machine learning; Molecular dynamics; Neural networks; Simulation; Sodium chloride; Solvation; Water treatment
• ISSN: 1549-9618; 1549-9626
• DOI: 10.1021/acs.jctc.3c00984

Molecular dynamics (MD) simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules. On the other hand, the models should be computationally efficient to enable simulations of large spatiotemporal scales. Here, we present the deep implicit solvation model for sodium chloride solutions that satisfies both requirements. Owing to the use of the neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive. We demonstrate our approach first for pure ionic solutions with concentrations ranging from physiological to 2 M. We then extend the model to capture the effective ion interactions in the vicinity and far away from a DNA molecule. In both cases, the structural properties are in good agreement with all-atom MD, showcasing a general methodology for the efficient and accurate modeling of ionic media.