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Computational Chemistry and Theory

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Computational chemistry employs quantum and classical approaches to simulate molecular properties and dynamics of chemical systems. C-CAS researchers utilize computer-based techniques (ex. molecular dynamics, coupled cluster, DFT) to study reaction mechanism, develop chemical descriptors, and generate data for training new machine learning models.

Publications

  • Torres, Jose Antonio Garrido; Lau, Sii Hong; Anchuri, Pranay; Stevens, Jason M; Tabora, Jose E; Li, Jun; Borovika, Alina; Adams, Ryan P; Doyle, Abigail G. A Multi-Objective Active Learning Platform and Web App for Reaction Optimization. J. Am. Chem. Soc. 2022, 144,43,19999-20007. https://pubs.acs.org/doi/10.1021/jacs.2c08592

  • Casetti, N.; Anstine, D.; Isayev, O.; Coley, C.W. Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials. J. Chem. Theor. Comp. 2025, 21, ASAP. https://doi.org/10.1021/acs.jctc.5c01161

  • Żurański, A.M.; Wang, J.Y.; Shields, B.J.; Doyle, A.G. Auto-QChem: an automated workflow for the generation and storage of DFT calculations for organic molecules. React. Chem. Engin. 2022, 7, 1276-1284  https://doi.org/10.1039/D2RE00030J

  • Luchini, Guilian, and Robert Paton. "Bottom-up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters." ACS Phys. Chem Au, 2024. ASAP https://pubs.acs.org/doi/10.1021/acsphyschemau.3c00045

  • Bartholomew, G.L.; Karas, L.J.; Eason, R.M.;Yeung, C.S.; Sigman, M.S.; Sarpong, R. Cheminformatic Analysis of Core-Atom Transformations in Pharmaceutically Relevant Heteroaromatics. J. Med. Chem. 2025, 68, 6027-6040. doi.org/10.1021/acs.jmedchem.4c02839

  • Sowndarya, S. S., Kim, Y., Kim, S., John, P. C. S., & Paton, R. S. Expansion of Bond Dissociation Prediction with Machine Learning to Medicinally and Environmentally Relevant Chemical Space. Digital Discovery. Digital Discovery, 2023, 2, 1900-1910. https://doi.org/10.1039/D3DD00169E

     

     

  • . Liu, Z.; Vinkus, J.; Fu, Y.; Liu, P.; Noonan, K. J. T.; Isayev, O. Fast and Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions. J. Am. Chem. Soc. Au 2025, 5, ASAP https://doi.org/10.1021/jacsau.5c00667

  • Luchini, G., Alegre-Requena, J. V., Funes-Ardoiz, I., Paton, R.S. GoodVibes: Automated thermochemistry for heterogeneous computational chemistry data. F1000Research, 2020, 9, 291. https://doi.org/10.12688/f1000research.22758.1

  • Gallegos, L.C.; Luchini, G.; St John, P.C.; Kim, S.; Paton, R.S. Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties Acc. Chem. Res. 2021, 54, 4, 827-836.   https://pubs.acs.org/doi/10.1021/acs.accounts.0c00745

  • Keto, A., Guo, T., Gonnheimer, N., Zhang, X., Krenske, E.H., Wiest, O. Improving reaction prediction through chemically aware transfer learning. Digital Discovery. DOI: 10.1039/d4dd00412d

  • Ickes, A.R., Liles, J.P., Borlinghaus, N., Henle, J., Swiatowiec, R., Prakash Kaushik, N., Braje, W.M., Harper, K.C., Shekhar, S. and Sigman, M.S., 2025. Leveraging Data Science to Elucidate Ligand Features for Pd-Catalyzed Enantioretentive N-Arylations of Cyclic α-Substituted Amines in Aqueous Media. J. Am. Chem. Soc. 2025, 147, ASAP https://doi.org/10.1021/jacs.5c07224

  • Yang, Y.; Zhang, S.; Ranasinghe, K.; Isayev, O.; Roitberg, A. “Machine Learning of Reactive Potentials.” ChemRxiv. 10.26434/chemrxiv-2023-x82fz.

  • Wright, Brandon A., Taku Okada, Alessio Regni, Guilian Luchini, Shree Sowndarya S. V, Nattawadee Chaisan, Sebastian Kölbl, Sojung F. Kim, Robert S. Paton, and Richmond Sarpong. "Molecular Complexity-Inspired Synthetic Strategies toward the Calyciphylline A-Type Daphniphyllum Alkaloids Himalensine A and Daphenylline." Journal of the American Chemical Society. (2024). ASAP https://doi.org/10.1021/jacs.4c11252

  • Sigmund LM, Sowndarya S, Albers A, Erdmann P, Paton RS, Greb L. Predicting Lewis Acidity: Machine‐Learning the Fluoride Ion Affinity of p‐Block‐Atom‐based Molecules. Angewandte Chemie International Edition. 2024 Mar 7:e202401084. https://doi.org/10.1002/anie.202401084

  • Haas, B., Hardy, M.A., Sowndarya, S.S., Adams, K., Coley, C.W., Paton, R.S. and Sigman, M.S., 2024. Rapid Prediction of Conformationally-Dependent DFT-Level Descriptors using Graph Neural Networks for Carboxylic Acids and Alkyl Amines. Digital Discovery. https://doi.org/10.1039/D4DD00284A

  • Guan, Y.; Sowndarya. S.S.; Gallegos, L.C.; St. John, P.C.; Paton, R.S., Real-Time Prediction of 1H and 13C Chemical Shifts with DFT Accuracy Using a 3D Graph Neural Network. Chem. Sci. 2021, 12, 12012-12026.   https://pubs.rsc.org/en/content/articlehtml/2021/sc/d1sc03343c

  • Jones, K.E.; Park, B.; Doering, N.A.; Baik, M.H.; Sarpong, R.  Rearrangements of the Chrysanthenol Core: Application to a Formal Synthesis of Xishacorene B. J. Am. Chem. Soc. 2021, 143, 20482–20490 https://doi.org/10.1021/jacs.1c10804

  • Fedik, N.; Nebgen, B.; Lubbers, N.; Barros, K.; Kulichenko, M.; Li, Y.W.; Zubatyuk, R.; Messerly, R.; Isayev, O.; Tretiak, S. "Synergy of semiempirical models and machine learning in computational chemistry." J. Chem. Phys. 2023, 159, 110901.  https://pubs.aip.org/aip/jcp/article/159/11/110901/2911476

  • Liu, Zhen, Yurii S. Moroz, and Olexandr Isayev. "The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions." Chem.  Sci. 2023, 14, 10835-10846.  doi https://doi.org/10.1039/D3SC03902A

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