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.

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 mechanisms, their efficiencies depend on the coformers investigated. The most important parameters characterizing the methods are the (a) screenable coformer fraction, (b) coformer success rate, (c) ability to give several cocrystals per successful coformer, (d) identification of new stable phases, and (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 30 molecules, predicted by a network-based link prediction algorithm (described in Cryst. Growth Des. 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 14 showing stability and 12 structures resolved.

<div>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>

The adsorption of carboxylic acid molecules at the calcite (104) and the muscovite (001) surface was investigated using surface X-ray diffraction. All four investigated carboxylic acid molecules, hexanoic acid, octanoic acid, lauric acid, and stearic acid, were found to adsorb at the calcite surface. Whereas the shortest two carboxylic acid molecules, hexanoic acid and octanoic acid, showed limited ordering and a flexible, disordered chain, the two longest carboxylic acid molecules form fully ordered monolayers, i.e., these form highly structured self-assembled monolayers. The latter molecules are oriented almost fully upright, with a tilt of up to 10°. The oxygen atoms of the organic molecules are found at similar positions to those of water molecules at the calcite–water interface. This suggests that in both cases, the oxygen atoms compensate for the broken bonds at the calcite surface. Under the same experimental conditions, stearic acid does not adsorb to K + and Ca 2+ -functionalized muscovite mica because the neutral molecules do not engage in the ionic bonds typical for the mica interface. These differences in adsorption behavior are characteristic for the differences of the oil–solid interactions in carbonate and sandstone reservoirs.