Cocrystals are predicted using a network of coformers extracted from the CSD.

As ignificant amount of attention has been given to the design and synthesis of co-crystals by both industry and academia because of its potential to change am olecules physicochemical properties.Y et, difficulties arise when searching for adequate combinations of molecules (or coformers) to form co-crystals,h ampering the efficient exploration of the targetss olid-state landscape.T his paper reports on the application of ad ata-driven co-crystal 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 ac ocrystal is likely to form. By combining the output of multiple models of both types,o ur approach shows to have excellent performance on the proposed co-crystal training and validation sets,and has an estimated accuracy of 80 %for molecules for which previous co-crystallization data is unavailable.

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.

To obtain a better understanding of which coformers to combine for the successful formation of a cocrystal, techniques from data mining and network science are used to analyze the data contained in the Cambridge Structural Database (CSD). A network of coformers is constructed based on cocrystal entries present in the CSD and its properties are analyzed. From this network, clusters of coformers with a similar tendency to form cocrystals are extracted. The popularity of the coformers in the CSD is unevenly distributed: a small group of coformers is responsible for most of the cocrystals, hence resulting in an inherently biased data set. The coformers in the network are found to behave primarily in a bipartite manner, demonstrating the importance of combining complementary coformers for successful cocrystallization. Based on our analysis, it is demonstrated that the CSD coformer network is a promising source of information for knowledge-based cocrystal prediction. research papers Acta Cryst. (2019). B75, 371-383 Devogelaer et al. Cocrystals in the CSD: a network approach 383

As ignificant amount of attention has been given to the design and synthesis of co-crystals by both industry and academia because of its potential to change am olecules physicochemical properties.Y et, difficulties arise when searching for adequate combinations of molecules (or coformers) to form co-crystals,h ampering the efficient exploration of the targetss olid-state landscape.T his paper reports on the application of ad ata-driven co-crystal 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 ac ocrystal is likely to form. By combining the output of multiple models of both types,o ur approach shows to have excellent performance on the proposed co-crystal training and validation sets,and has an estimated accuracy of 80 %for molecules for which previous co-crystallization data is unavailable.