Feb 5, 2021 · In this work, a novel method based on graph neural networks to predict the solubility of a molecule in any generic organic solvent has been ...
In this work, we present a deep learning method based on graph networks to accurately predict solvation free energies of small organic molecules ...
This work presents a graph neural network (GNN) for the prediction of ΔGsolv which, in addition to encoding typical atom and bond-level features, incorporates ...
[PDF] Predicting solvation free energies with an implicit solvent machine ... - arXiv
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May 31, 2024 · Learning atomic interactions through solvation free energy prediction using graph neural networks. J. Chem. Inf. Model. 61, 689–698 (2021) ...
Nov 28, 2022 · This work presents a graph neural network (GNN) for the prediction of ΔGsolv which, in addition to encoding typical atom and bond-level features ...
Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks. Authors: Yashaswi Pathak,Sarvesh Mehta,U Deva Priyakumar
In this paper, we propose a method based on graph neural networks using molecular graph representation of molecules to predict solvation free energies that is ...
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Explainable Solvation Free Energy Prediction Combining Graph Neural ...
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Nov 28, 2022 · Solute-solvent interactions are included via an interaction map layer which can be visualized to examine solubility-enhancing or -decreasing ...
Here, we introduce a novel, machine-learning based quantitative structure-property prediction method which predicts solvation free energies for various organic ...
The topology of atoms and bonds is regarded as a graph which is sent to the network, and the node features are updated through the message passing mechanism. ..