A simple pharmacophore point filter has been developed that discriminates between drug-like and nondrug-like chemical matter. It is based on the observation that nondrugs are often underfunctionalized. Therefore, a minimum count of well-defined pharmacophore points is required to pass the filter. The application of the filter results in 66-69% of subsets of the MDDR database to be classified as drug-like. Furthermore, 61-68% of subsets of the CMC database are classified as drug-like. In contrast, only 36% of the ACD are found to be drug-like. While these results are not quite as good as those obtained with recently described neural net approaches, the method used here has clear advantages. In contrast to a neural net approach and also in contrast to decision tree methods described recently, the pharmacophore filter has been developed by using "chemical wisdom" that is unbiased from fitting the structural content of specific drug databases to prediction models. Similar to decision tree methods, the pharmacophore point filter provides a detailed structural reason for the classification of each molecule as drug or nondrug. The pharmacophore point filter results are compared to neural net filter results. A statistically significant overlap between compounds recognized as drug-like validates both approaches. The pharmacophore point filter complements neural net approaches as well as property profiling approaches used as drug-likeness filters in compound library analysis and design.

With the increasing production of metabolomic data there is an awareness of a need for a standardised description of this data to aid assessment, exchange, storage and curation of information from metabolomic studies. In this manuscript the first draft of a minimum requirement for the description of the biological context of a metabolomic study involving mammalian subjects is described. This recommendation has been produced by the Metabolomics Standards Initiative-Mammalian Context Working Sub-Group (MSI-MCWSG) as part of the wider standardisation initiative led by the Metabolomics society. The experiments considered include functional genomic studies, drug toxicology, nutrigenomics, clinical trials, and other human studies. Two reporting requirements are described for pre-clinical (e.g. functional genomics, toxicology) and clinical (e.g. clinical trials, nutrigenomics) studies. It is planned that this will lead to the development of a tool for the description of metabolomic experiments that enables storage, retrieval and manipulation of large amounts of data. This will benefit the assessment and dissemination of metabolomic data from mammalian studies.

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New procedures for the rapid purification and covalent labeling of the (3-adrenergic receptors have been developed that should greatly accelerate progress in the study of these widely distributed adenylate cyclase-coupled receptors. Chromatography of solubilized receptor preparations on a Sepharosealprenolol affinity gel followed by HPLC on steric exclusion columns lead to rapid (2 days) and high yield ("-

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