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• W4224058524 endingPage "1848" @default.
• W4224058524 startingPage "1840" @default.
• W4224058524 abstract "Graph neural network (GNN)-based deep learning (DL) models have been widely implemented to predict the experimental aqueous solvation free energy, while its prediction accuracy has reached a plateau partly due to the scarcity of available experimental data. In order to tackle this challenge, we first build a large and diverse calculated data set Frag20-Aqsol-100K of aqueous solvation free energy with reasonable computational cost and accuracy via electronic structure calculations with continuum solvent models. Then, we develop a novel 3D atomic feature-based GNN model with the principal neighborhood aggregation (PNAConv) and demonstrate that 3D atomic features obtained from molecular mechanics-optimized geometries can significantly improve the learning power of GNN models in predicting calculated solvation free energies. Finally, we employ a transfer learning strategy by pre-training our DL model on Frag20-Aqsol-100K and fine-tuning it on the small experimental data set, and the fine-tuned model A3D-PNAConv-FT achieves the state-of-the-art prediction on the FreeSolv data set with a root-mean-squared error of 0.719 kcal/mol and a mean-absolute error of 0.417 kcal/mol using random data splits. These results indicate that integrating molecular modeling and DL would be a promising strategy to develop robust prediction models in molecular science. The source code and data are accessible at: https://yzhang.hpc.nyu.edu/IMA." @default.
• W4224058524 created "2022-04-19" @default.
• W4224058524 creator A5061354710 @default.
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• W4224058524 creator A5079817016 @default.
• W4224058524 date "2022-04-14" @default.
• W4224058524 modified "2023-10-12" @default.
• W4224058524 title "Accurate Prediction of Aqueous Free Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer Learning" @default.
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• W4224058524 doi "https://doi.org/10.1021/acs.jcim.2c00260" @default.
• W4224058524 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35422122" @default.
• W4224058524 hasPublicationYear "2022" @default.
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