One class classification as a practical approach for accelerating π–π co-crystal discovery
Aikaterini Vriza, Angelos B. Canaj, Rebecca Vismara, Laurence J. Kershaw Cook, Troy D. Manning, Michael W. Gaultois, P.A. Wood, Vitaliy Kurlin, Neil G. Berry, Matthew S. Dyer, Matthew J. Rosseinsky
Abstract
Machine learning using one class classification on a database of existing co-crystals enables the identification of co-formers which are likely to form stable co-crystals, resulting in the synthesis of two co-crystals of polyaromatic hydrocarbons.
Topics & Concepts
InterpretabilityClass (philosophy)Chemical spaceComputer scienceRanking (information retrieval)WorkflowDrug discoveryMachine learningData miningArtificial intelligenceChemistryDatabaseBiochemistryMachine Learning in Materials ScienceCrystallography and molecular interactionsComputational Drug Discovery Methods