ReviewJournal of chemical information and modeling2022
Accurate Prediction of Aqueous Free Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer Learning.
Review in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A diverse and chemically relevant solvation model benchmark set with flexible molecules and conformer ensembles.Chemical science · 2025Article
- Fast and Accurate Prediction of Tautomer Ratios in Aqueous Solution via a Siamese Neural Network.Journal of chemical theory and computation · 2025Article
- From Implicit to Explicit: An Interaction-Reorganization Approach to Molecular Solvation Energy.Journal of chemical theory and computation · 2024Article
- ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction.Journal of chemical information and modeling · 2024Article
- Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks.Journal of chemical theory and computation · 2024Article
- Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy.Journal of chemical information and modeling · 2024Article
- Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates.Chemical science · 2024Article
- Integrated Molecular Modeling and Machine Learning for Drug Design.Journal of chemical theory and computation · 2023Review
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations.RSC advances · 2023Article
- Multitask Deep Ensemble Prediction of Molecular Energetics in Solution: From Quantum Mechanics to Experimental Properties.Journal of chemical theory and computation · 2023Article
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Authors and funding
3 authors.
Funding
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.
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