In drug design, it is crucial to have reliable information on how a chemical entity behaves in the presence of metabolizing enzymes. This requires substantial experimental efforts. Consequently, being able to predict the likely site/s of metabolism in any compound, synthesized or virtual, would be highly beneficial and time efficient. In this work, six different methodologies for predictions of the site of metabolism (SOM) have been compared and validated using structurally diverse data sets of drug-like molecules with well-established metabolic pattern in CYP3A4, CYP2C9, or both. Three of the methods predict the SOM based on the ligand's chemical structure, two additional methods use structural information of the enzymes, and the sixth method combines structure and ligand similarity and reactivity. The SOM is correctly predicted in 50 to 90% of the cases, depending on method and enzyme, which is an encouraging rate. We also discuss the underlying mechanisms of cytochrome P450 metabolism in the light of the results from this comparison.

A 'global' model of hERG K(+) channel was built to satisfy three basic criteria for QSAR models in drug discovery: (1) assessment of the applicability domain, (2) assuring that model decisions can be interpreted by medicinal chemists and (3) assessment of model performance after the model was built. A combination of D-optimal onion design and hierarchical partial least squares modelling was applied to construct a global model of hERG blockade in order to maximize the applicability domain of the model and to enhance its interpretability. Additionally, easily interpretable hERG specific fragment-based descriptors were developed. Model performance was monitored, throughout a time period of 15 months, after model implementation. It was found that after this time duration a greater proportion of molecules were outside the model's applicability domain and that these compounds had a markedly higher average prediction error than those from molecules within the model's applicability domain. The model's predictive performance deteriorated within 4 months after building, illustrating the necessity of regular updating of global models within a drug discovery environment.

We evaluate prospects for the crystal engineering of anionic halocuprate complexes [Cu n X m ] z2 , X~Cl, Br, I, crystallised with polyatomic cations that possess alkyl or aryl coated surfaces and do not form hydrogen bonds. In solution, halocuprate complexes are involved in kinetically fast dissociative and associative equilibria, as well as redox variation between Cu(I), mixed Cu(I)/Cu(II) and Cu(II), and so the species that crystallise need not be those that predominate in solution: we describe instances of this complication, and the trapping of complexes which are not evident in solution. Our survey includes the relationships between cation properties and the identity and structure of the crystallised halocuprate complex, and the occurrence of polymorphism and de-facto polymorphism. A key aspect is the packing arrangement of anions and cations in these crystals: we illustrate the primary influence of electrostatic energies in determination of crystal packing, and the secondary but significant influences of cation??cation motifs and embraces in controlling details of crystal structure.In the context of crystal engineering, for which fundamental understanding and control of the crystallisation process and of the crystal structure are prerequisites, we conclude that even a chemically simple coordination system such as [Cu n X m ] z2 needs additional knowledge and physico-chemical insight before there can be confident design and reliable fabrication of desired crystals. Progress will be aided by the publication (in crystal structure papers) of more information about crystallising solutions and the effects of crystallisation variables, as well as ability to repeat crystallisation.Scheme 2 The crystallisation of a complex Z which has negligible equilibrium concentration in solution, but very low solubility.

Predictive metabolism methods can be used in drug discovery projects to enhance the understanding of structure-metabolism relationships. The present study uses data mining methods to exploit biotransformation data that have been recorded in the MDL Metabolite database. Reacting center fingerprints were derived from a comparison of substrates and their corresponding products listed in the database. This process yields two fingerprint databases: all atoms in all substrates and all reacting centers. The metabolic reaction data are then mined by submitting a new molecule and searching for fingerprint matches to every atom in the new molecule in both databases. An "occurrence ratio" is derived from the fingerprint matches between the submitted compound and the reacting center and substrate fingerprint databases. Normalization of the occurrence ratio within each submitted molecule enables the results of the search to be rank-ordered as a measure of the relative frequency of a reaction occurring at a specific site within the submitted molecule. Predictive performance that would allow this method to be used by drug discovery teams to generate useful hypotheses regarding structure metabolism relationships was observed.