Phase II publications

Phase II Publications (since Sept 2022)

    1. 44. Matthews, A.D., Peters, E., Debenham, J.S., Gao, Q., Nyamiaka, M.D., Pan, J., Zhang, L.K., Dreher, S.D., Krska, S.W., Sigman, M.S. and Uehling, M.R., 2023. Cu Oxamate-Promoted Cross-Coupling of α-Branched Amines and Complex Aryl Halides: Investigating Ligand Function through Data Science. ACS Catalysis, 13(24), 16195-16206. doi https://doi.org/10.1021/acscatal.3c04566

      43. Raghavan, P.; Haas, B.C.; Ruos, M.E.; Schleinitz, J.; Doyle, A.G.; Reisman, S.E.; Sigman, M.S.; Coley, C.W. Dataset Design for Building Models of Chemical Reactivity ACS Cent. Sci 2023, 9, ASAP https://doi.org/10.1021/acscentsci.3c01163.

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

      41. Bartholomew, G.L.; Kraus, S.L.; Karas, L.J.; Carpaneto, F.; Bennett, R.; Sigman, M.S.; Yeung, C.S.; Sarpong, R. “14N to 15N Isotopic Exchange of Nitrogen Heteroaromatics through Skeletal Editing” ChemRxiv 2023 .10.26434/chemrxiv-2023-30dtw

      40. Ziyi Kou , Shichao Pei , Yijun Tian and Xiangliang Zhang A Controllable Prompt Adversarial Attacking Framework for Black-Box Text Guided Image Generation Models 2023 Intl. Joint Conf. Art Intel https://www.ijcai.org/proceedings/2023/0109.pdf

      39. 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://doi.org/10.1063/5.0151833

      38. Guo, Taicheng, Kehan Guo, Zhengwen Liang, Zhichun Guo, Nitesh V. Chawla, Olaf Wiest, and Xiangliang Zhang. "What can GPT models do in chemistry? A comprehensive benchmark on eight tasks." NeurIPS 2023, accepted. https://doi.org/10.48550/arXiv.2305.18365

      37. Santhanalakkshmi Vejaykummar, S. S.; Kim, Y.; Kim, S.; St. John, P.; Paton, R. Expansion of Bond Dissociation Prediction with Machine Learning to Medicinally and Environmentally Relevant Chemical Space ChemRxiv 2023. 10.26434/chemrxiv-2023-d147w

      36. Wang, J. Y.; Stevens, J. M.; Kariofillis, S. K.; Tom, M.-J.; Li, J.; Tabora, J. E.; Parasram, M.; Shields, B.; Primer, D.; Hao, B.; Del Valle, D.; DiSomma, S.; Furman, A.; Zipp, G. G.; Melnikov, S.; Paulson, J.; Doyle, A. Reinforcement learning prioritizes general applicability in reaction optimization ChemRxiv 2023 10.26434/chemrxiv-2023-dcg9d

      35. 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." ChemRxiv (2023) 10.26434/chemrxiv-2023-j9h92

      34. Luchini, Guilian, and Robert Paton. "Bottom-up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters." ChemRxiv (2023) 10.26434/chemrxiv-2023-n8jsm-v2.

      33. Casetti, N., Alfonso-Ramos, J.E., Coley, C.W. and Stuyver, T., Combining Molecular Modeling and Machine Learning for Accelerated Reaction Screening and Discovery. Chemistry Europ. J., 2023 p.e202301957. https://doi.org/10.1002/chem.202301957

      32. Guo, Zhichun, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, and Nitesh V. Chawla. "Boosting graph neural networks via adaptive knowledge distillation." In Proceedings of the AAAI Conference on Artificial Intelligence, 2023 37, 7793-7801. https://doi.org/10.1609/aaai.v37i6.25944

      31. van Dijk, Lucy, Brittany C. Haas, Ngiap-Kie Lim, Kyle Clagg, Jordan J. Dotson, Sean M. Treacy, Katarzyna A. Piechowicz et al. "Data Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides." J. Am. Chem. Soc. 2023, 145 ASAP. https://doi.org/10.1021/jacs.3c06674

      30. Ortiz, K.; Dotson, J.; Robinson, D. J.; Sigman, M. S.; Karimov, R. R. “Catalyst-controlled enantioselective and regiodivergent addition of aryl boron nucleophiles to N-alkyl nicotinate salts,” J. Am. Chem. Soc. 2023, 145, accepted for publication.

      29. Maloney, M. P., Coley, C.W.; Genheden, S.; Carson, N.; Helquist, P. Norrby, P.-O.; Wiest, O. "Negative Data in Data Sets for Machine Learning Training." Org. Let. 2023, 25, 2945–2947 https://doi.org/10.1021/acs.orglett.3c01282. Published in parallel in J. Org. Chem. 2023, 88, 5239–5241 https://doi.org/10.1021/acs.joc.3c00844

      28. Guo, Z.; Nan, B.; Tian, Y.; Wiest, O.; Zhang, C.; Chawla, N.V. “Graph-based Molecular Representation Learning” Intl. Joint Conf. Art. Int. 2023 accepted https://doi.org/10.48550/arXiv.2207.04869