Video: Solubility prediction using crystallographic information

Richard Marchese Robinson, a post-doctoral research fellow at the University of Leeds School of Chemical and Process Engineering describes his work in the ADDoPT project looking at the effect of incorporating crystallographic information into statistical modelling of temperature dependent solubility of organic, crystalline materials.

Richard's work has involved evaluating the inclusion of both lattice energies and 3D descriptors calculated from an experimental crystal structure into the models, and also comparing models for the enthalpy of solution based on molecular descriptors with or without melting point values, and examining the effect of including melting point values into direct predictions of temperature dependent solubility. The models developed can help inform the digital design of manufacturing unit operations such as cooling crystallisation and wet granulation.

A detailed description of this work and the results arising has recentyl been published ( The influence of solid state information and descriptor selection on statistical models of temperature dependent aqueous solubility Richard L. Marchese Robinson, Kevin J. Roberts and Elaine B. Martin  J Cheminform (2018) 10: 44).