Horwitz, M. A.; Dürr, A. B.; Afratis, K.; Chen, Z.; Soika, J.; Christensen, K. E.; Fushimi, M.; Paton, R. S.; Gouverneur, V. J. Am. Chem. Soc. 2023, DOI: 10.1021/jacs.3c01303
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
Luo, C.; Alegre-Requena, J. V.; Sujansky, S. J.; Pajk, S.; Gallegos, L. C.; Paton, R. S.; Bandar, J. S. J. Am. Chem. Soc. 2022, 144, 9586–9596
Sowndarya, S. S. V.; St. John, P. C.; Paton, R. S.Chem. Sci. 2021, 12, 13158-13166.
Guan, Y.; Sowndarya, S. S. V.; Gallegos, L. C.; St. John, P. C.; Paton, R. S. Chem. Sci. 2021, 12, 12012-12026.
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.
DBSTEP is a python package for obtaining DFT-Based Steric Parameters from 3-dimensional chemical structures. It can parse the outputs from most computational chemistry programs and other common molecular structure file formats. Steric properties can either be obtained exactly or by using a Cartesian grid, the latter approach being amenable to the featurization of a molecular isodensity surface (DBSTEP can process wavefunction files) rather than using classical atomic radii. Currently, traditional Sterimol parameters (L, Bmin, Bmax) and percent buried volume parameters are implemented, as well as our novel steric parameter vectors Sterimol2vec and vol2vec. This package is designed for use on the command line or alternatively implemented in a Python script for use in a computational workflow to collect steric parameters.
Gallegos, L. C.; Luchini, G.; St. John, P. C.; Kim, S.; Paton, R. S. Acc. Chem. Res. 2021, 54, 827–836
A Python program to compute quasi-harmonic thermochemical data and potential energy surface diagrams from frequency calculations at a given temperature/concentration, corrected for the effects of vibrational scaling-factors. All (electronic, translational, rotational and vibrational) partition functions are recomputed and can be correct to any temperature or concentration. The first public version of GoodVibes was released in 2016 and it has undergone several revisions since, during which time it has been used by many groups around the world. The program is described in the publication: GoodVibes: automated thermochemistry for heterogeneous computational chemistry data
