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• W3199883944 abstract "Implicit solvent models, such as Poisson-Boltzmann models, play important roles in computational studies of biomolecules. A vital step in almost all implicit solvent models is to determine the solvent-solute interface, and the solvent excluded surface (SES) is the most widely used interface definition in these models. However, classical algorithms used for computing SES are geometry-based, so that they are neither suitable for parallel implementations nor convenient for obtaining surface derivatives. To address the limitations, we explored a machine learning strategy to obtain a level set formulation for the SES. The training process was conducted in three steps, eventually leading to a model with over 95% agreement with the classical SES. Visualization of tested molecular surfaces shows that the machine-learned SES overlaps with the classical SES in almost all situations. Further analyses show that the machine-learned SES is incredibly stable in terms of rotational variation of tested molecules. Our timing analysis shows that the machine-learned SES is roughly 2.5 times as efficient as the classical SES routine implemented in Amber/PBSA on a tested central processing unit (CPU) platform. We expect further performance gain on massively parallel platforms such as graphics processing units (GPUs) given the ease in converting the machine-learned SES to a parallel procedure. We also implemented the machine-learned SES into the Amber/PBSA program to study its performance on reaction field energy calculation. The analysis shows that the two sets of reaction field energies are highly consistent with a 1% deviation on average. Given its level set formulation, we expect the machine-learned SES to be applied in molecular simulations that require either surface derivatives or high efficiency on parallel computing platforms." @default.
• W3199883944 created "2021-09-27" @default.
• W3199883944 creator A5031137530 @default.
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• W3199883944 creator A5058824947 @default.
• W3199883944 date "2021-09-13" @default.
• W3199883944 modified "2023-10-17" @default.
• W3199883944 title "Machine-Learned Molecular Surface and Its Application to Implicit Solvent Simulations" @default.
• W3199883944 cites W1596947964 @default.
• W3199883944 cites W1967292572 @default.
• W3199883944 cites W1967809254 @default.
• W3199883944 cites W1971317029 @default.
• W3199883944 cites W1976937534 @default.
• W3199883944 cites W1977115694 @default.
• W3199883944 cites W1979762151 @default.
• W3199883944 cites W1988115241 @default.
• W3199883944 cites W1988613096 @default.
• W3199883944 cites W1993828173 @default.
• W3199883944 cites W1994737524 @default.
• W3199883944 cites W2000613080 @default.
• W3199883944 cites W2001502878 @default.
• W3199883944 cites W2006566482 @default.
• W3199883944 cites W2006930542 @default.
• W3199883944 cites W2020002894 @default.
• W3199883944 cites W2028031397 @default.
• W3199883944 cites W2028753963 @default.
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• W3199883944 cites W2035284816 @default.
• W3199883944 cites W2058653824 @default.
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• W3199883944 cites W2063831812 @default.
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• W3199883944 cites W2073146967 @default.
• W3199883944 cites W2078290150 @default.
• W3199883944 cites W2083665636 @default.
• W3199883944 cites W2093834886 @default.
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• W3199883944 cites W2136799255 @default.
• W3199883944 cites W2145899756 @default.
• W3199883944 cites W2153187042 @default.
• W3199883944 cites W2160784118 @default.
• W3199883944 cites W2169113748 @default.
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• W3199883944 doi "https://doi.org/10.1021/acs.jctc.1c00492" @default.
• W3199883944 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34516109" @default.
• W3199883944 hasPublicationYear "2021" @default.
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