Tag: machine learning

Real-time Prediction of 1H and 13C Chemical Shifts with DFT accuracy using a 3D Graph Neural Network.

Guan, Y.; Sowndarya, S. S. V.; Gallegos, L. C.; St. John, P. C.; Paton, R. S. Chem. Sci. 2021, 12, 12012-12026.

CASCADE.

CASCADE stands for ChemicAl Shift CAlculation with DEep learning. It is a stereochemically-aware graph network for the prediction of NMR chemical shifts. Model training was performed against 8,000 DFT structures followed by transfer learning with experimental  spectra. A web-server has been created to access CASCADE predictions from SMILES or by drawing structures in the graphical interface. An automated workflow executes 3D structure embedding and MMFF conformer searching. The full ensemble of optimized conformations are passed to a trained graph neural network to predict the NMR chemical shifts (in ppm) for C and H atoms. The underlying datasets used for training and the Python code to run CASCADE from the command line have been made available.

[GitHub]
Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties.

Gallegos, L. C.; Luchini, G.; St. John, P. C.; Kim, S.; Paton, R. S. Acc. Chem. Res. 2021, 54, 827–836

Prediction of homolytic bond dissociation enthalpies for organic molecules at near chemical accuracy with sub-second computational cost.

St John, P.; Guan, Y.; Kim, Y.; Kim, S.; Paton, R. S. Nat. Commun. 2020, 11, 2328

Quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules.

St John, P.; Guan, Y.; Kim, Y.; Etz, B. D.; Kim, S.; Paton, R. S. Scientific Data 2020, 7, 244