Will they co-crystallize?. Issue 36 (12th July 2017)

• Record Type: Journal Article
• Authors: Wicker, Jerome G. P.; Crowley, Lorraine M.; Robshaw, Oliver; Little, Edmund J.; Stokes, Stephen P.; Cooper, Richard I.; Lawrence, Simon E.
• Is Part Of: CrystEngComm. Volume 19:Issue 36(2017)
• Page Start: 5336
• Page End: 5340
• Publication Date: 2017-07-12
• Subjects: Crystals -- Periodicals; Crystal growth -- Periodicals; Crystallography -- Periodicals; Cristaux -- Périodiques; Cristaux -- Croissance -- Périodiques; Cristallographie -- Périodiques; 548
• DOI: 10.1039/c7ce00587c
• Languages: English
• ISSNs: 1466-8033

Abstract

Abstract : Co-crystal screening data and machine learning models allows prediction of the most likely co-formers to use for new molecules. 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.