One of the biggest barriers when it comes to studying the structures of molecules is the ability to obtain them in a crystalline form for x-ray diffraction. Now, Richard Cooper and Jerome Wicker at the University of Oxford, UK, have developed a machine learning approach to predict whether a small organic molecule will be able to crystallise. Since crystallinity is vital both for determining structures, and also for the delivery of many drugs, this work could provide valuable information.
0χv was found to give the highest predictive accuracy in determining crystallisation propensity
Machine learning involves the construction of algorithms that can learn from data, and it has been used in the past to predict the solubilities and melting points of materials. Cooper and Wicker set out to test whether simple two-dimensional information, such as atom types, bond types and molecular volume, could be used to predict if a material would crystallise.
Interested? Read the full story at Chemistry World.
The original article can be read below:
Will it crystallise? Predicting crystallinity of molecular materials
Jerome G. P. Wicker and Richard I. Cooper
CrystEngComm, 2015, Advance Article
DOI: 10.1039/C4CE01912A
