The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices' for performing CSP calculations. All of the targets, apart from a single potentially disorderedZ′ = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.

Polymerization of isocyanopeptides results in the formation of high molecular mass polymers that fold in a proteinlike fashion to give helical strands in which the peptide chains are arranged in β-sheets. The β-helical polymers retain their structure in water and unfold in a cooperative process at elevated temperatures. The peptide architecture in these polymers is a different form of the β-helix motif found in proteins. Unlike their natural counterparts, which contain arrays of large β-sheets stacked in a helical fashion, the isocyanopeptide polymers have a central helical core that acts as a director for the β-sheet–like arrangement of the peptide side arms. The helical structure of these isocyanopeptide polymers has the potential to be controlled through tailoring of the side branches and the hydrogen-bonding network present in the β-sheets.

The design of pharmaceutical cocrystals has initiated widespread debate on the classification of cocrystals. Current attempts to classify multicomponent crystals suffer from ambiguity, which has led to inconsistent definitions for cocrystals and for multicomponent crystals in general. Inspired by the work of Aitipamula et al. (Cryst. Growth Des. 2012, 12, 2147–2152), we present a feasible classification system for all multicomponent crystals. The present classification enables us to analyze and classify multicomponent crystal structures present in the Cambridge Structural Database (CSD). This reveals that all seven classes proposed are relevant in terms of frequency of occurrence. Lists of CSD refcodes for all classes are provided. We identified over 5000 cocrystals in the CSD, as well as over 12 000 crystals with more than two components. This illustrates that the possibilities for alternative drug formulations can be increased significantly by considering more than two components in drug design.

A significant 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 a molecule's physicochemical properties. Yet, difficulties arise when searching for adequate combinations of molecules (or coformers) to form co‐crystals, hampering the efficient exploration of the target's solid‐state landscape. This paper reports on the application of a data‐driven co‐crystal prediction method based on two types of artificial neural network models and co‐crystal data present in the Cambridge Structural Database. The models accept pairs of coformers and predict whether a co‐crystal is likely to form. By combining the output of multiple models of both types, our 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.