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

• 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

• 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, 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