Tag: GNN

Rapid Prediction of Conformationally-Dependent DFT-Level Descriptors using Graph Neural Networks for Carboxylic Acids and Alkyl Amines.

Haas, B. C.; Hardy, M. A.; Sowndarya S. V., S.; Adams, K.; Coley, C. W.; Paton, R. S.; Sigman, M. S. in revision 2024, DOI: 10.26434/chemrxiv-2024-m5bpn

Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries.

Sowndarya, S. S. V.; Law, J.; Tripp, C.; Duplyakin, D.; Skordilis, E.; Biagioni, D.; Paton, R. S.; St. John, P. C. Nat. Mach. Intell. 2022, 7, 720–730

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]