Two novel models, based on the Graph Neural Network (GNN) architectures, are proposed, capable of making accurate predictions of the molecular properties ...

Jul 31, 2021 · ... hydration free energy. Such results are obtained from a simple, graph-convolution based neural network instead of deep learning models in ...

... Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer Learning. ... in predicting calculated solvation free energies. Finally ...

Feb 22, 2023 · ... Learning atomic interactions through solvation free energy prediction using graph neural networks. Journal of Chemical Information and ...

May 1, 2023 · Accurate prediction of aqueous free solvation energies using 3D atomic feature-based graph neural network with transfer learning. J. Chem ...

Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks · Chemistry, Computer Science. J. Chem. Inf. Model. · 2021.

Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks. 2021 •. Yashaswi Pathak. Download Free PDF View PDF.

May 21, 2021 · However, because the model relies on human-engineered atomic fingerprints, its prediction accuracy is limited. In this study, we present a graph ...

We tested the architecture (which we call SolvGNN) on a comprehensive phase equilibrium case study that aims to predict activity coefficients for a wide range ...

Dive into the research topics of 'Explainable Solvation Free Energy Prediction Combining Graph Neural Networks with Chemical Intuition'. Together they form ...