1. Dong, J., Zhao, M., Liu, Y., Su, Y., Zeng, X. Deep learning in retrosynthesis planning: datasets, models and tools. Briefings in Bioinformatics 2022, 23.
2. Liu, C.-H., Korablyov, M., Jastrzebski, S., Wlodarczyk-Pruszynski, P., Bengio, Y., Segler, M. RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software. Journal of Chemical Information and Modeling 2022, 62, 2293-300.
3. Yan, C., Zhao, P., Lu, C., Yu, Y., Huang, J. RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction. Biomolecules 2022, 12.
4. Baylon, J.L., Cilfone, N.A., Gulcher, J.R., Chittenden, T.W. Enhancing Retrosynthetic Reaction Prediction with Deep Learning Using Multiscale Reaction Classification. Journal of Chemical Information and Modeling 2019, 59, 673-88.
5. Thakkar, A., Chadimova, V., Bjerrum, E.J., Engkvist, O., Reymond, J.-L. Retrosynthetic accessibility score (RAscore) - rapid machine learned synthesizability classification from AI driven retrosynthetic planning. Chemical Science 2021, 12, 3339-49.
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