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4 days ago · 216 have demonstrated the ability to replace traditional rule-based discrete atom-typing schemes with continuous atomic representations generated by neural.
6 days ago · In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using Gaussian ...
2 days ago · In this project, we provide a deep-learning neural network (DNN) based biophysics model to predict protein properties. The model uses multi-scale and ...
1 day ago · An integrated approach that combines superimposition techniques and deep neural networks is demonstrated in this study to leverage the power of deep learning ...
7 days ago · Machine-learned interatomic potentials (MILPs) are rapidly gaining interest for molecular modeling, as they provide a balance between quantum-mechanical ...
7 days ago · Solvent-Specific Featurization for Predicting Free Energies of Solvation through Machine Learning ... Chemistry-Intuitive Explanation of Graph Neural Networks for ...
6 days ago · Spectra Prediction and Peak Assignment using Graph Neural Networks . ... the mean absolute deviation (MAD) of the solvation free energy by up to 4.5 kcal mol.
2 days ago · The calculation of accurate free energies lies at the core of investigating biomolecular processes such as protein-ligand binding, protein-antibody interactions ...
3 days ago · Our method, termed Network Binding Free Energy (NetBFE), performs adaptive binding free energy calculations in iterations, re-optimizing the allocations in each ...
4 days ago · Materials are often represented in machine learning applications by (chemical-)geometric descriptions of their atomic structure. In this work, we propose an ...
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