Prediction of Mefenamic Acid Crystal Shape by Random Forest Classification.

ABSTRACT:

Objective

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.

Methods

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.

Results

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 wi

SUBMITTER: Nakapraves S 

PROVIDER: S-EPMC9780130 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature