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Phase II publications

Phase II Publications (since Sept 2022)

    1. 52. Romer, N.P., Min, D.S., Wang, J.Y., Walroth, R.C., Mack, K.A., Sirois, L.E., Gosselin, F., Zell, D., Doyle, A.G. and Sigman, M.S., 2024. Data Science Guided Multiobjective Optimization of a Stereoconvergent Nickel-Catalyzed Reduction of Enol Tosylates to Access Trisubstituted Alkenes. ACS Catalysis, 14, pp.4699-4708. https://doi.org/10.1021/acscatal.4c00650

    2. 51. Chen J, Guo K, Liu Z, Isayev O, Zhang X. Uncertainty-Aware Yield Prediction with Multimodal Molecular Features. In Proceedings of the AAAI Conference on Artificial Intelligence 2024 Mar 24 (Vol. 38, No. 8, pp. 8274-8282).

    3. 50. 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

    4. 49. Wang JY, Stevens JM, Kariofillis SK, Tom MJ, Golden DL, Li J, Tabora JE, Parasram M, Shields BJ, Primer DN, Hao B. Identifying general reaction conditions by bandit optimization. Nature. 2024, 626, 1025-1033. https://doi.org/10.1038/s41586-024-07021-y

    5. 48. Yanqiao Zhu, Jeehyun Hwang, Keir Adams, Zhen Liu, Bozhao Nan, Brock Stenfors, Yuanqi Du, Jatin Chauhan, Olaf Wiest, Olexander Isayev, Connor Coley, Yizhou Sun, Wei Wang. Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks. International Conference on Learning Representations (ICLR) 2024. https://sxkdz.github.io/files/publications/ICLR/MARCEL/MARCEL.pdf

    6. 47. 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. https://doi.org/10.1021/acscatal.3c04566

    7. 46. Boiko, D.A., MacKnight, R., Kline, B. and Gomes, G., 2023. Autonomous chemical research with large language models. Nature, 624(7992), 570-578. doi: 10.1038/s41586-023-06792-0

    8. 45. 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

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

    10. 43. 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” J. Am. chem. Soc. 2024, 146, 5, 2950-2958. https://doi.org/10.1021/jacs.3c11515https://doi.org/10.1021/jacs.3c11515

    11. 42. Kou, Z.;  Pei, S.; Tian, Y.; Zhang, X., Character as pixels: A Controllable Prompt Adversarial Attacking Framework for Black-Box Text Guided Image Generation Models. In Proceedings of the 32nd Internatioanl Joint Confernce on Artificial Intelligence (IJCAI 2023) 2023 983-990 https://www.ijcai.org/proceedings/2023/0109.pdf

      41. 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

      40. Guo, T.; Guo, K.; Nan, B.; Liang, Z.; Guo, Z.; Chawla, N.V.; Wiest, O.; and Zhang, X. "What can large language models do in chemistry? A comprehensive benchmark on eight tasks."NeurIPS 2023, accepted. https://doi.org/10.48550/arXiv.2305.18365

      39. 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. Digital Discovery, 2023, 2, 1900-1910. doi: https://doi.org/10.1039/D3DD00169E

      38. 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

      37. 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, 12, 10835-10846. doi: https://doi.org/10.1039/D3SC03902A

      36. 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://doi.org/10.1021/acsphyschemau.3c00045

      35. 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

      34. 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

      33.  van Dijk, L.; Haas, B.C.; Lim, N.; Clagg,K.; Dotson, J.J.; Treacy, S.M.; Piechowicz, K.A., 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

      32. 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, 11781-11788 https://doi.org/10.1021/jacs.3c03048

      31. 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

      30. Guo, Z.; Guo, K.; Nan, B.; Tian, Y.; Iyer, R.G.; Ma, Y.; Wiest, O.; Zhang, Wang, W.; 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

      29. Ma, C.; Yang, Q.; Gao, X.; Zhang, X. DEMO: Disentangled Molecular Graph Generation via an Invertible Flow Model. In: CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022, 1420-1429. https://doi.org/10.1145/3511808.3557217