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Chemical substructures in drug discovery

Serono Pharmaceutical Research Institute, 14, ch. des Aulx, 1228-Plan-les-Ouates, Geneva, Switzerland.
Drug Discovery Today (Impact Factor: 5.96). 08/2003; 8(13):594-602. DOI: 10.1016/S1359-6446(03)02740-5
Source: PubMed

ABSTRACT The widespread use of HTS and combinatorial chemistry techniques has led to the generation of large amounts of pharmacological data, which, in turn, has catalyzed the development of computational methods designed to reduce the time and cost in identifying molecules suitable for pharmaceutical development. This review focuses on the use of substructure-based in silico techniques for lead discovery, an effective and increasingly popular approach for augmenting the chance of selecting drug-like compounds for preclinical and clinical development.

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    • "The model was then applied to predict the top three most likely protein targets for compounds from the MDDR database and it was found that on average, it was 77% correct at target identification. A database containing thousands of substructures annotated to over 500 biological endpoints, including pharmacological, cell-based, animal model-based, toxicity, ADME and therapeutic outcomes, has been reported (Merlot et al., 2003). The substructural database is then used as an end point alert for molecules containing any of the substructures catalogued in the database associated with a given end point. "
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    ABSTRACT: Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.
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