Representation Learning
When building an ML model, it is not clear which features need to be included in the model. Representation Learning (RL) allows the unbiased selection of features should be included in the model. When applied to chemical reactions, analysis of the features selected by RL can provide new insights into the reaction. C-CAS is developing new RL algorithms and applying them for the elucidation of reaction mechanisms, e.g. for Ni-catalyzed cross couplings
Publications
Huang, X., Surve, M., Liu, Y, Luo, T., Wiest, O., Zhang, X., Chawla, N.V. Application of large language, models in chemistry reaction, data extraction, and cleaning. ACM Boise, ID CIKM 2024, ACM, Boise, ID Oct. 21-25. doi.acm.org?doi=3627673.3679874
Guo, K., Nan, B., Zhou, Y., Guo, T., Guo, Z., Surve, M., Liang, Z., Chawla, N.V., Wiest, O., Zhang, X. Can LLMs Solve Molecular Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation. NeurPS 2024 https://nips.cc/virtual/2024/poster/97472
Casetti, N.; Nevatia, P.; Chen, J.; Schwaller, P.; Coley, C. W. Comment on "Molecular hypergraph neural networks” J. Chem. Phys. 2024, 161, 207101 https://doi.org/10.1063/5.0239722
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
Zhichun Guo, Kehan Guo, Bozhao Nan, Yijun Tian, Roshni G. Iyer, Yihong Ma, Olaf Wiest, Xiangliang Zhang, Wei Wang, Chuxu Zhang, Nitesh V. Chawla. Graph-based Molecular Representation Learning. Intl. Joint Conf. Art. Int. 2023 , pp 6638-6646. https://doi.org/10.24963/ijcai.2023/744
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
Tang P, Jiang M, Xia BN, Pitera JW, Welser J, Chawla NV. Multi-label patent categorization with non-local attention-based graph convolutional network. Proc. AAAI Conf. Art. Int. 2020 34, 9024-9031. https://ojs.aaai.org/index.php/AAAI/article/view/6435
Dong, Kaiwen, Zhichun Guo, and Nitesh Chawla. Pure message passing can estimate common neighbor for link prediction. Advances in Neural Information Processing Systems 37 (2025): 73000-73035.
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