Crystal structure prediction for organic molecules requires both the fast assessment of thousands to millions of crystal structures and the greatest possible accuracy in their relative energies. We describe a crystal lattice simulation program, DMACRYS, emphasizing the features that make it suitable for use in crystal structure prediction for pharmaceutical molecules using accurate anisotropic atom-atom model intermolecular potentials based on the theory of intermolecular forces. DMACRYS can optimize the lattice energy of a crystal, calculate the second derivative properties, and reduce the symmetry of the spacegroup to move away from a transition state. The calculated terahertz frequency k = 0 rigid-body lattice modes and elastic tensor can be used to estimate free energies. The program uses a distributed multipole electrostatic model (Q, t = 00,...,44s) for the electrostatic fields, and can use anisotropic atom-atom repulsion models, damped isotropic dispersion up to R(-10), as well as a range of empirically fitted isotropic exp-6 atom-atom models with different definitions of atomic types. A new feature is that an accurate model for the induction energy contribution to the lattice energy has been implemented that uses atomic anisotropic dipole polarizability models (alpha, t = (10,10)...(11c,11s)) to evaluate the changes in the molecular charge density induced by the electrostatic field within the crystal. It is demonstrated, using the four polymorphs of the pharmaceutical carbamazepine C(15)H(12)N(2)O, that whilst reproducing crystal structures is relatively easy, calculating the polymorphic energy differences to the accuracy of a few kJ mol(-1) required for applications is very demanding of assumptions made in the modelling. Thus DMACRYS enables the comparison of both known and hypothetical crystal structures as an aid to the development of pharmaceuticals and other speciality organic materials, and provides a tool to develop the modelling of the intermolecular forces involved in molecular recognition processes.

The results of the fifth blind test of crystal structure prediction, which show important success with more challenging large and flexible molecules, are presented and discussed.

We report on the organization and outcome of the fourth blind test of crystal structure prediction, an international collaborative project organized to evaluate the present state in computational methods of predicting the crystal structures of small organic molecules. There were 14 research groups which took part, using a variety of methods to generate and rank the most likely crystal structures for four target systems: three single-component crystal structures and a 1:1 cocrystal. Participants were challenged to predict the crystal structures of the four systems, given only their molecular diagrams, while the recently determined but as-yet unpublished crystal structures were withheld by an independent referee. Three predictions were allowed for each system. The results demonstrate a dramatic improvement in rates of success over previous blind tests; in total, there were 13 successful predictions and, for each of the four targets, at least two groups correctly predicted the observed crystal structure. The successes include one participating group who correctly predicted all four crystal structures as their first ranked choice, albeit at a considerable computational expense. The results reflect important improvements in modelling methods and suggest that, at least for the small and fairly rigid types of molecules included in this blind test, such calculations can be constructively applied to help understand crystallization and polymorphism of organic molecules.

Following the interest generated by two previous blind tests of crystal structure prediction (CSP1999 and CSP2001), a third such collaborative project (CSP2004) was hosted by the Cambridge Crystallographic Data Centre. A range of methodologies used in searching for and ranking the likelihood of predicted crystal structures is represented amongst the 18 participating research groups, although most are based on the global minimization of the lattice energy. Initially the participants were given molecular diagrams of three molecules and asked to submit three predictions for the most likely crystal structure of each. Unlike earlier blind tests, no restriction was placed on the possible space group of the target crystal structures. Furthermore, Z' = 2 structures were allowed. Part-way through the test, a partial structure report was discovered for one of the molecules, which could no longer be considered a blind test. Hence, a second molecule from the same category (small, rigid with common atom types) was offered to the participants as a replacement. Success rates within the three submitted predictions were lower than in the previous tests - there was only one successful prediction for any of the three ;blind' molecules. For the ;simplest' rigid molecule, this lack of success is partly due to the observed structure crystallizing with two molecules in the asymmetric unit. As in the 2001 blind test, there was no success in predicting the structure of the flexible molecule. The results highlight the necessity for better energy models, capable of simultaneously describing conformational and packing energies with high accuracy. There is also a need for improvements in search procedures for crystals with more than one independent molecule, as well as for molecules with conformational flexibility. These are necessary requirements for the prediction of possible thermodynamically favoured polymorphs. Which of these are actually realised is also influenced by as yet insufficiently understood processes of nucleation and crystal growth.

Use of high-throughput, in vitro bioactivity data in setting a point-of-departure (POD) has the potential to accelerate the pace of human health safety evaluation by informing screening-level assessments. The primary objective of this work was to compare PODs based on high-throughput predictions of bioactivity, exposure predictions, and traditional hazard information for 448 chemicals. PODs derived from new approach methodologies (NAMs) were obtained for this comparison using the 50th (PODNAM, 50) and the 95th (PODNAM, 95) percentile credible interval estimates for the steady-state plasma concentration used in in vitro to in vivo extrapolation of administered equivalent doses. Of the 448 substances, 89% had a PODNAM, 95 that was less than the traditional POD (PODtraditional) value. For the 48 substances for which PODtraditional < PODNAM, 95, the PODNAM and PODtraditional were typically within a factor of 10 of each other, and there was an enrichment of chemical structural features associated with organophosphate and carbamate insecticides. When PODtraditional < PODNAM, 95, it did not appear to result from an enrichment of PODtraditional based on a particular study type (eg, developmental, reproductive, and chronic studies). Bioactivity:exposure ratios, useful for identification of substances with potential priority, demonstrated that high-throughput exposure predictions were greater than the PODNAM, 95 for 11 substances. When compared with threshold of toxicological concern (TTC) values, the PODNAM, 95 was greater than the corresponding TTC value 90% of the time. This work demonstrates the feasibility, and continuing challenges, of using in vitro bioactivity as a protective estimate of POD in screening-level assessments via a case study.