Prediction of mefenamic acid crystal shape by random forest classification

Siya Nakapraves, Monika Warzecha, Chantal L. Mustoe, Vijay Srirambhatla, Alastair J. Florence (Editor)

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
15 Downloads (Pure)



Particle shape can have a significant impact on the bulk properties of materials. This study describes the development and application of machine-learning models to predict the crystal shape of mefenamic acid recrystallized from organic solvents.


Crystals were grown in 30 different solvents to establish a dataset comprising solvent molecular descriptors, process conditions and crystal shape. Random forest classification models were trained on this data and assessed for prediction accuracy.


The highest prediction accuracy of crystal shape was 93.5% assessed by fourfold cross-validation. When solvents were sequentially excluded from the training data, 32 out of 84 models predicted the shape of mefenamic acid crystals for the excluded solvent with 100% accuracy and a further 21 models had prediction accuracies from 50-100%. Reducing the feature set to only solvent physical property descriptors and supersaturations resulted in higher overall prediction accuracies than the models trained using all available or another selected subset of molecular descriptors. For the 8 solvents on which the models performed poorly (ConclusionsRandom forest classification models using solvent physical property descriptors can reliably predict crystal morphologies for mefenamic acid crystals grown in 20 out of the 28 solvents included in this work. Poor prediction accuracies for the remaining 8 solvents indicate that further factors will be required in the feature set to provide a more generalized predictive morphology model.
Original languageEnglish
Pages (from-to)3099-3111
Number of pages13
JournalPharmaceutical Research
Issue number12
Early online date19 Dec 2022
Publication statusE-pub ahead of print - 19 Dec 2022


  • mefenamic acid
  • crystallisation
  • random forest classification
  • crystal shape prediction


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