CrystEngComm, 2017, 19, 5336 – 5340 [ doi:10.1039/C7CE00587C ]
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.
- Publisher’s copy: RSC
- ORA: ORA record and copy