The program Mercury, developed by the Cambridge Crystallographic Data Centre, is designed primarily as a crystal structure visualization tool. A new module of functionality has been produced, called the Materials Module, which allows highly customizable searching of structural databases for intermolecular interaction motifs and packing patterns. This new module also includes the ability to perform packing similarity calculations between structures containing the same compound. In addition to the Materials Module, a range of further enhancements to Mercury has been added in this latest release, including void visualization and links to ConQuest, Mogul and IsoStar.

Since its original release, the popular crystal structure visualization program Mercury has undergone continuous further development. Comparisons between crystal structures are facilitated by the ability to display multiple structures simultaneously and to overlay them. Improvements have been made to many aspects of the visual display, including the addition of depth cueing, and highly customizable lighting and background effects. Textual and numeric data associated with structures can be shown in tables or spreadsheets, the latter opening up new ways of interacting with the visual display. Atomic displacement ellipsoids, calculated powder diffraction patterns and predicted morphologies can now be shown. Some limited molecular-editing capabilities have been added. The object-oriented nature of the C++ libraries underlying Mercury makes it easy to re-use the code in other applications, and this has facilitated three-dimensional visualization in several other programs produced by the Cambridge Crystallographic Data Centre.

Two new programs have been developed for searching the Cambridge Structural Database (CSD) and visualizing database entries: ConQuest and Mercury. The former is a new search interface to the CSD, the latter is a high-performance crystal-structure visualizer with extensive facilities for exploring networks of intermolecular contacts. Particular emphasis has been placed on making the programs as intuitive as possible. Both ConQuest and Mercury run under Windows and various types of Unix, including Linux.

The bonding and conformational characteristics of bissulfonylamides and analogous imides m·e compm·ed. Structures (44 altogether) of type R-S02-NQ-S02-R' (R, R' =Me, Et, or Aryl) were rettieved from the Cambridge Structural Database (Mm·ch 1994 release, 120481 ent1ies). They me either neutral (Q = H, alkyl and m·yl group or hetero atom like 0 and S) or chmged (Q = e-or metal, like In, Hg, etc.).Analysis of the S-N bonds varying in the range 1.57-1.76 A and their observed ini1uence upon the other S-X and X-S-Y pmameters enabled us:i. to chm·actetize the S-N bonds against the differences in the nitrogen enviroment (chm-ged N, N-metal and N-covalent bonds),ii. to prove the applicability of the bond order conservation principle (Johnston, 1961) and the VSEPR rules (Gillespie, 1963) on the S(VI) [O,O' ,N,C] iii. to describe the intra-and intermolecular conditions of the folded conformation vs the energetically favourable open form of the C-S02-NQ-S02-C moieties, iv. and to substantiate the inequalities of the 0-S-N angles which is attributable to the synclinal position of one of the S-0 bonds with N-lone pair (Kalman eta!., 1981).The mm·ked difference between the mchetypes of the S-N bonds indicates that the interdependence of the S-X bonds in the NS02C tetrahedra me ptincipally governed by the environment of the nitrogen atoms. With the rapid growth of crystallographic databases. fully automatic methods for mining knowledge hom these databases me needed. Several classit]cation algorithms m·e already incorporated into the databases. While these have greatly facilitated the analysis and classification of datasets, considerable user intervention is still required. For example, extensive examination of the dataset may be needed for the selection of clustering alg01ithm, data pm·ameters, similarity measure, similm·ity threshold, stopping point, etc. Furthermore, different choices of algotithms and metJ.ics often yield different results. It is thus important to evaluate the robustness of the results and assess their possible dependence on artifacts of the approach used. Thus, a fully automated classification approach requires methods for both pre-classification data preview and post-classification result assessment. This contribution presents a method for the automatic determination of str-uctural subclasses in datasets rettieved from the CSD. Subclasses/ clusters me obtained by undergoing a comprehensive automated data preview which is followed by applying clustering alg01ithms and then by undergoing post-clustering evaluation of the results. The automatic preview component is based on a comprehensive analysis of histograms and scattergrams generated for potential classification pm·ameters. This process helps identify informative pmameters and gives a prelitninm·y clustering of the dataset. For postclassification evaluation, plots of a clustering similarity index are used to assess how the results m·e affected by different algorithms and by the introduction of random noise into the dataset. These plots help understan...