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Found 5 papers, 2 papers with code
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery.
Transfer learning for solvation free energies: from quantum chemistry to experiments
This is a significant improvement compared to the mean absolute error of the quantum calculations (0. 40 kcal/mol).
Transfer Learning
Chemical Physics
Message Passing Networks for Molecules with Tetrahedral Chirality
Molecules with identical graph connectivity can exhibit different physical and biological properties if they exhibit stereochemistry-a spatial structural characteristic.
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction
Advances in deep neural network (DNN) based molecular property prediction have recently led to the development of models of remarkable accuracy and generalization ability, with graph convolution neural networks (GCNNs) reporting state-of-the-art performance for this task.
A graph-convolutional neural network model for the prediction of chemical reactivity
We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).
