Generative Learning
Generative learning in chemistry allows the prediction of new molecules based on the models build e.g. for properties of a known set of molecules. This allows for example the automated design of new catalysts with improved properties. Approaches to generative learning explored in C-CAS include genetic algorithms in CoDECs and the use of large language models (LLMs).
Publications
Ma, C., Guo, T., Yang, Q., Chen, X., Gao, X., Liang, S., Chawla, N., Zhang, X. A Property-Guided Diffusion model for Generating Molecular Graphs. April 14, 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi 10.1109/ICASSP48485.2024.10447350.
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
Huang, Y.; Jiang, Z.; Luo, X.; Guo, K.; Zhuang, H.; Zhou, Y.; Yuan, Z.; Sun, X.; Schleinitz, J.; Wang, Y.; Zhang, S.; Surve, M.; Chawla, N. V.; Wiest, O.; Zhang, X. ChemOrch: Empowering LLMs with Chemical Intelligence via Groundbreaking Synthetic Instructions. NeuIPS 2025 accepted
Le, K., Guo, Z., Dong, K., Huang, X., Nan, B., Iyer, R., Zhang, X., Wiest, O., Wang, W.; Hua, T.; Chawla, N.V. MolX: Enhancing Large Language Models for Molecular Learning with A Multi-Modal Extension. Proc. 2025 ACM SIGKDD Intl. Conf. Knowl. Disc. Data Min (MLoG-GenAI@KDD ’25) 2025 https://doi.org/10.48550/arXiv.2406.06777
Guo, Z., Yu, W., Zhang, C., Jiang, M. and Chawla, N.V. GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction. Proc. 29th ACM Intl. Conf. Inf. Knowl. Manag. 2020, 435-443. https://dl.acm.org/doi/10.1145/3340531.3411981
Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N.V., Wiest, O., Zhang, X. Large Language Model based Multi-Agents: A Survey of Progress and Challenges. International Joint Conference on Artificial Intelligence (IJCAI 2024), survey track. Jeju Island, South Korea, August 3-9, 2024. (Acceptance rate of 20%, 48 out of 232 submissions)
MacKnight, R.; Boiko, D. A.; Regio J. E.; Gallegos, L.C.; Neukomm, T.A, Gomes, G. Rethinking chemical research in the age of large language models Nature Comp. Sci. 2025, doi.org/10.1038/s43588-025-00811-y
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