Machine learning-guided strategies for reaction conditions design and optimization

Lung-Yi Chen and Yi-Pei Li
Beilstein J. Org. Chem. 2024, 20, 2476–2492. https://doi.org/10.3762/bjoc.20.212

Cite the Following Article

Machine learning-guided strategies for reaction conditions design and optimization
Lung-Yi Chen and Yi-Pei Li
Beilstein J. Org. Chem. 2024, 20, 2476–2492. https://doi.org/10.3762/bjoc.20.212

How to Cite

Chen, L.-Y.; Li, Y.-P. Beilstein J. Org. Chem. 2024, 20, 2476–2492. doi:10.3762/bjoc.20.212

Download Citation

Citation data can be downloaded as file using the "Download" button or used for copy/paste from the text window below.
Citation data in RIS format can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Zotero.

Presentation Graphic

Picture with graphical abstract, title and authors for social media postings and presentations.
Format: PNG Size: 11.0 MB Download

Citations to This Article

Up to 20 of the most recent references are displayed here.

Scholarly Works

  • Chalasani, A. S.; Deb, S.; Anand, A.; Li, Y.; Downing, R.; Njoo, E. Evaluation of Machine Learning Models for Condition Optimization in Diverse Amide Coupling Reactions. ACS Omega 2026. doi:10.1021/acsomega.6c01866
  • Zhang, Y.; Fang, Y.; Zhou, H.; Yu, B.; Fung, T. F.; Liu, Q.; Len, C.; Gao, H. ProcedureT5: adaptive experimental procedure prediction with data-augmented pre-training and multi-source data integration. Reaction Chemistry & Engineering 2026, 11, 1584–1598. doi:10.1039/d5re00572h
  • Chawla, N. V.; González-Montiel, G. A.; Guo, K.; Guo, T.; Hua, T.; Huang, X.; Inae, E.; Jiang, M.; Le, K.; Liu, G.; Maier, J. C.; Moniz, N.; Nogueira, B.; Pan, D.; Piguave, B. V.; Savoie, B. M.; Schofield, A. B.; Shen, Y.; Taylor, A.; Zhang, X.; Zhu, Y.; Wiest, O. Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms. Chemical reviews 2026, 126, 7587–7635. doi:10.1021/acs.chemrev.5c01081
  • Kang, Y.; Kilari, H.; Nazemifard, N.; Renner, C. B.; Yang, Y.; Papageorgiou, C.; Nagy, Z. K. Population balance modeling and digital design of degree of agglomeration in industrial crystallization. AIChE Journal 2026. doi:10.1002/aic.70502
  • Khuzaifa, M.; Kainat, I.; Zeng, Q. Applications of AI in Organic Chemistry. ChemistrySelect 2026, 11. doi:10.1002/slct.73624
  • Alvarado, D.; Johnston, B.; Brown, C. Deep generative models for pharmaceutical manufacturing process design. Chemical Engineering Research and Design 2026, 230, 571–585. doi:10.1016/j.cherd.2026.05.001
  • Furukawa, T.; Yamada, R.; Maeda, S.; Matsuoka, W. A Virtual Model for Describing Organophosphorus Reactivity: Validation and Application to Virtual Molecule-Assisted Optimization. Journal of computational chemistry 2026, 47, e70396. doi:10.1002/jcc.70396
  • Ranasinghe, S.; Yavari, A.; Thathsara, T.; Harrison, C. J.; Shafiei, M. Machine learning applications in semiconductor metal oxide chemiresistive hydrogen sensing: A review. International Journal of Hydrogen Energy 2026, 235, 155132. doi:10.1016/j.ijhydene.2026.155132
  • Lee, Y.-A.; Yeh, C.-Y.; Li, Y.-P. Joint optimization of molecular structures and process conditions using variational autoencoders and swarm-based metaheuristics. Journal of the Taiwan Institute of Chemical Engineers 2026, 187, 106741. doi:10.1016/j.jtice.2026.106741
  • Wang, H.; Zhang, Y.; Wang, Z.; Zhuang, J.; Cheng, Z.; Qian, Y.; Zhou, A.; Peng, S.; He, X. Enhancing Diversity of Template-Free Retrosynthesis Prediction via Hierarchical Latent Variables. Journal of chemical information and modeling 2026, 66, 3074–3090. doi:10.1021/acs.jcim.5c03128
  • Lamas, W. d. Q.; Grandinetti, F. J. Bayesian optimisation for enhanced hydrogen production and thermal energy systems: Advancing efficiency and sustainability. International Journal of Hydrogen Energy 2026, 218, 154002. doi:10.1016/j.ijhydene.2026.154002
  • Li, S.; Chen, S.; Oliveira, J. C. A.; Zhang, S.; Ackermann, L.; Hong, X. Stufenweises diversitätsbeschränktes maschinelles Lernen für die Optimierung hochdimensionaler Reaktionsbedingungen. Angewandte Chemie 2026, 138. doi:10.1002/ange.4418883
  • Li, S.-W.; Chen, S.; Oliveira, J. C. A.; Zhang, S.-Q.; Ackermann, L.; Hong, X. Staged Diversity-Constrained Machine Learning for High-Dimensional Reaction Condition Optimization. Angewandte Chemie (International ed. in English) 2026, 65, e4418883. doi:10.1002/anie.4418883
  • McDonald, M. A.; Jensen, K. F. Machine Learning and Autonomous Systems for Accelerated Synthesis. Annual review of analytical chemistry (Palo Alto, Calif.) 2026, 19, 331–353. doi:10.1146/annurev-anchem-071924-103847
  • Herrera-Acevedo, C.; Menezes, R. P.; Scotti, L.; Scotti, M. T. Cheminformatics in life cycle assessment: Advancing solvent, toxicology, and chemical synthesis for sustainable innovation. Cheminformatic Modeling and Data Gap Filling for a Green and Sustainable Environment; Elsevier, 2026; pp 951–974. doi:10.1016/b978-0-443-36474-7.00037-5
  • Usman, M.; Waseem, S.; Hassan, T.; Imran, M. Predictive modeling for chemical processes. Applied Machine Learning in Chemical Process Engineering; Elsevier, 2026; pp 41–57. doi:10.1016/b978-0-443-33943-1.00008-3
  • Zhao, P.-C.; Zhou, H.-Z.; Wang, Q.; Wu, Z.-Y.; Yu, H.; Shi, J.-Y. A comprehensive survey of AI-based retrosynthesis planning: Datasets, models, and tools. The Innovation Informatics 2026, 2, 100026. doi:10.59717/j.xinn-inform.2026.100026
  • Yan, X.; Zhong, H.; Wang, X. Robust Chemical Reaction Condition Recommendations via Label Mix Strategy. Journal of chemical information and modeling 2025, 65, 12775–12785. doi:10.1021/acs.jcim.5c02054
  • Casillo, E.; Scattolin, T.; Nolan, S. P. Catalysis meets machine learning: a guide to data-driven discovery and design. Chemical communications (Cambridge, England) 2025, 61, 18247–18272. doi:10.1039/d5cc05274b
  • Schweidtmann, A. M.; Schwaller, P. Adaptive experimentation and optimization in organic chemistry. Beilstein journal of organic chemistry 2025, 21, 2367–2368. doi:10.3762/bjoc.21.180
Other Beilstein-Institut Open Science Activities