A random forest model for predicting crystal packing of olanzapine solvates

Rajni M. Bhardwaj, Susan M. Reutzel-Edens, Blair F. Johnston, Alastair J. Florence

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
26 Downloads (Pure)


A random forest model obtained from calculated physicochemical properties of solvents and observed crystallised structures of olanzapine has for the first time enabled the prediction of different types of 3-dimensional crystal packings of olanzapine solvates. A novel olanzapine solvate was obtained by targeted crystallization from the solvent identified by the random forest classification model. The model identified van der Waals volume, number of covalent bonds and polarisability of the solvent molecules as key contributors to the 3-D crystal packing type of the solvate.

Original languageEnglish
Pages (from-to)3947-3950
Number of pages4
Issue number28
Publication statusPublished - 24 Apr 2018


  • random forest model
  • crystal packing
  • olanzapine solvates
  • physicochemical properties
  • Machine Learning


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