Abstract
A data-driven approach to predicting co-crystal formation reduces the number of experiments required to successfully produce new co-crystals. A machine learning algorithm trained on an in-house set of co-crystallization experiments results in a 2.6-fold enrichment of successful co-crystal formation in a ranked list of co-formers, using an unseen set of paracetamol test experiments.
Cite
CITATION STYLE
APA
Wicker, J. G. P., Crowley, L. M., Robshaw, O., Little, E. J., Stokes, S. P., Cooper, R. I., & Lawrence, S. E. (2017). Will they co-crystallize? CrystEngComm, 19(36), 5336–5340. https://doi.org/10.1039/c7ce00587c
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