Machine learning implicit solvation for molecular dynamics
نویسندگان
چکیده
Accurate modeling of the solvent environment for biological molecules is crucial computational biology and drug design. A popular approach to achieve long simulation time scales large system sizes incorporate effect in a mean-field fashion with implicit models. However, challenge existing models that they often lack accuracy or certain physical properties compared explicit models, as many-body effects neglected difficult model mean field. Here, we leverage machine learning (ML) multi-scale coarse graining (CG) order learn can approximate energetic thermodynamic given arbitrary accuracy, enough training data. Following previous ML--CG CGnet CGSchnet, introduce ISSNet, graph neural network, potential force. ISSNet from data be readily applied MD simulations. We compare solute conformational distributions under different solvation treatments two peptide systems. The results indicate outperform widely-used generalized Born surface area reproducing thermodynamics small protein systems respect solvent. success this novel method demonstrates benefit applying methods accurate silico research biomedical applications.
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ژورنال
عنوان ژورنال: Journal of Chemical Physics
سال: 2021
ISSN: ['1520-9032', '1089-7690', '0021-9606']
DOI: https://doi.org/10.1063/5.0059915