Cocrystals in the Cambridge Structural Database: a network approach

Contains fulltext : 204870pub.pdf (publisher's version ) (Open Access

Cocrystal Prediction by Artificial Neural Networks

A significant amount of attention has been given to the design and synthesis of cocrystals by both industry and academia because of its potential to change a molecule’s physicochemical properties. This paper reports on the application of a data-driven cocrystal prediction method, based on two types of artificial neural network models and cocrystal data present in the Cambridge Structural Database. The models accept pairs of coformers and predict whether a cocrystal is likely to form.</div

Cocrystal design by network-based link prediction

Contains fulltext : 214377.pdf (publisher's version ) (Open Access

Comparing and quantifying the efficiency of cocrystal screening methods for Praziquantel

Pharmaceutical cocrystals are highly interesting due to their effect on physicochemical properties and their role in separation technologies, particularly for chiral molecules. Detection of new cocrystals is a challenge and robust screening methods are required. As numerous techniques exist that differ in their crystallization mechanism, their efficiencies depend on the coformers investigated. The most important parameters characterizing the methods are the a) screenable coformers fraction, b) coformers success rate, c) ability to give several cocrystals per successful coformer, d) identification of new stable phases, e) experimental convenience. Based on these parameters, we compare and quantify the performance of three methods: liquid-assisted grinding, solvent evaporation, and saturation temperature measurements of mixtures. These methods were used to screen thirty molecules, predicted by a network-based link prediction algorithm (described in Crystal Growth & Design 2021 21 (6), 3428-3437) as potential coformers for the target molecule Praziquantel. The solvent evaporation method presented more drawbacks than advantages, liquid-assisted grinding emerged as the most successful and the quickest, while saturation temperature measurements provided equally good results in a slower route yielding additional solubility information relevant for future screenings, single-crystal growth and cocrystal production processes. Seventeen cocrystals were found, with fourteen showing stability, and twelve structures resolved

Cocrystals of Praziquantel : discovery by network-based link prediction

Cocrystallization has been promoted as an attractive early development tool as it can change the physicochemical properties of a target compound and possibly enable the purification of single enantiomers from racemic compounds. In general, the identification of adequate cocrystallization candidates (or coformers) is troublesome and hampers the exploration of the solid-state landscape. For this reason, several computational tools have been introduced over the last two decades. In this study, cocrystals of Praziquantel (PZQ), an anthelmintic drug used to treat schistosomiasis, are predicted with network-based link prediction and experimentally explored. Single crystals of 12 experimental cocrystal indications were grown and subjected to a structural analysis with single-crystal X-ray diffraction. This case study illustrates the power of the link-prediction approach and its ability to suggest a diverse set of new coformer candidates for a target compound when starting from only a limited number of known cocrystals

Co-crystals of Praziquantel: Discovery by Network-Based Link Prediction

In this study, co-crystals for Praziquantel (PZQ), an anthelmintic drug used to treat schistosomiasis, are predicted with network-based link prediction. Single crystals of twelve co-crystal indications were grown and subjected to a structural analysis with single-crystal X-ray diffraction