Explainable Models
A broad acceptance of ML methods in chemistry requires an alignment of the models with the principles of chemistry and the generation of new physical insights. Many of the ML models developed focus on explainability such as the threshold analysis of the use of counterfactuals to understand the origin of model performance.
Publications
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
Andrzej M. Żurański, Shivaani S. Gandhi, and Abigail G. Doyle. A machine learning approach to model interaction effects: development and application to alcohol deoxyfluorination. J. Am. Chem. Soc. 2023, 145,14,7898-7909. https://doi.org/10.1021/jacs.2c13093
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
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://pubs.acs.org/doi/10.1021/acsphyschemau.3c00045
Stenfors, B.A.; Cadge, J. A.; Aikonen, S.; Luchini, G.; Wahlers, J.; Koh, K. H.; Murronen, M.; Menche, M.; Pfeifle, M.; Keto, A.; Paton, R.; Sigman, M.S.; Wiest, O. “Conformation Dependent Features of Bisphosphine Ligand.” J. Org. Chem 2025, 90, 13874–13884 doi.org/10.1021/acs.joc.5c01682
DOI of dataset(s): doi.org/10.5281/zenodo.17086568
Silva, J. D. J.; Bartalucci, N.; Jelier, B.; Grosslight, S.; Gensch, T.; Schünemann, C.; Müller, B.; Kamer, P. C.; Copéret, C.; Sigman, M. S., Development and Molecular Understanding of a Pd-catalyzed Cyanation of Aryl Boronic Acids Enabled by High-Throughput Experimentation and Data Analysis. Helv. Chim. Acta 2021. https://doi.org/10.1002/hlca.202100200
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
Newman-Stonebraker, Samuel; Smith, Sleight; Borowski, Julia; Peters, Ellyn; Gensch, Tobias; Johnson, Heather; Sigman, Matthew; Doyle, Abigail. Linking Mechanistic Analysis of Catalytic Reactivity Cliffs to Ligand Classification. ChemRxiv, May12, 2021. https://doi.org/10.26434/chemrxiv.14388557.v1
Yang, Y.; Zhang, S.; Ranasinghe, K.; Isayev, O.; Roitberg, A. “Machine Learning of Reactive Potentials.” ChemRxiv. 10.26434/chemrxiv-2023-x82fz.
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