Report on the sixth blind test of organic crystal-structure prediction methods

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 submissions, 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 disordered Z` = 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

FIDDLE. Simultaneous Indexing and Structure Solution from Powder Diffraction Data using a Genetic Algorithm and Correlation Functions

A method for the determination of crystal structures from powder diffraction data is presented that circumvents the difficulties associated with separate indexing. For the simultaneous optimization of the parameters that describe a crystal structure a genetic algorithm is used together with a pattern matching technique based on auto and cross correlation functions.<br /

Racemic and Enantiopure Camphene and Pinene Studied by the Crystalline Sponge Method

Contains fulltext : 187716.pdf (publisher's version ) (Open Access

Cocrystals in the Cambridge Structural Database: a network approach

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

2. The multiple phenyl embrace as a synthon in Cu(I)/PPh 3/N-donor ligand coordination polymers

The multiple phenyl embrace is a supramolecular motif comprised of phenyl-phenyl interactions, which can, like hydrogen bonds, form extended networks between molecules in the solid state. The analysis of 23 crystal structures of coordination polymers based on the M/PPh3/N-donor ligand system (M = Cu(I) or Ag(I)) showed that 71% of the independent M-PPh 3 groups are involved in a 6-fold phenyl embrace (6PE). Strong 6PE interactions are obtained when the geometry of the PPh3 group can be described as a rotor. The analysis of these groups showed that 83% of the PPh3 groups have their phenyl groups in the rotor conformation. It is shown, however, that these good rotors are not necessarily involved in the 6PE and that the 6PE can also be formed by nonrotors. In the Cu(I)/PPh 3/N-donor ligand system, the 6PE interactions form an independent connection (often) perpendicular to the backbone of the coordination polymer. In many cases, the 6PE increases the dimensionality of the network formed between Cu(I) and N-donor ligands. Therefore, the multiple phenyl embrace seems to be a useful synthon in crystal engineering of stable networks

The conformation of the idopyranose ring revisited: How subtle O-substituent induced changes can be deduced from vicinal H-1-NMR coupling constants

Contains fulltext : 225082.pdf (postprint version ) (Open Access

Polymorphism and Modulation of Para-Substituted L-Phenylalanine

Contains fulltext : 181130.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