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 ...
Feb 5, 2021 · In this work, we present a deep learning method based on graph networks to accurately predict solvation free energies of small organic molecules ...
Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks. https://doi.org/10.1021/acs.jcim.0c01413. Journal: Journal ...
Explainable Solvation Free Energy Prediction Combining Graph Neural ...
pubmed.ncbi.nlm.nih.gov › ...
Nov 28, 2022 · Solute-solvent interactions are included via an interaction map layer which can be visualized to examine solubility-enhancing or -decreasing ...
May 31, 2024 · Learning atomic interactions through solvation free energy prediction using graph neural networks. J. Chem. Inf. Model. 61, 689–698 (2021) ...
Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks. J. Chem. Inf. Model. Pub Date : 2021-02-05
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 ...
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 ...
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 ...