Chemical substructures in drug discovery
Serono Pharmaceutical Research Institute, 14, ch. des Aulx, 1228-Plan-les-Ouates, Geneva, Switzerland. Drug Discovery Today
(Impact Factor: 6.69).
08/2003; 8(13):594-602. DOI: 10.1016/S1359-6446(03)02740-5
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.
Available from: Monika Chauhan
- "Toxicophore discovery tools (Table 1) are advanced systems that selectively predict and determine a toxicophore. They have been defined as any formal system, not necessarily computer based, that enables a user to obtain rational predictions about the toxicity of chemicals (Merlot et al. 2003). "
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ABSTRACT: Toxicity is a common drawback of newly designed chemotherapeutic agents. With the exception of
pharmacophore-induced toxicity (lack of selectivity at higher concentrations of a drug), the toxicity due to
chemotherapeutic agents is based on the toxicophore moiety present in the drug. To date, methodologies
implemented to determine toxicophores may be broadly classified into biological, bioanalytical and
computational approaches. The biological approach involves analysis of bio-activated metabolites, whereas the
computational approach involves a QSAR-based method, mapping techniques, an inverse docking technique
and a few toxicophore identification/estimation tools. While, being one of the major steps in drug discovery
process, toxicophore identification has proven to be an essential screening step in drug design and development.
The paper is first of its kind, attempting to cover and compare different methodologies employed in predicting
and determining toxicophores with an emphasis on their scope and limitations. Such information may prove
vital in the appropriate selection of methodology and can be used as screening technology by researchers to
discover the toxicophoric potentials of their designed and synthesized moieties. Additionally, it can be utilized
in the manipulation of molecules containing toxicophores in such a manner that their toxicities might be
eliminated or removed
Archives of Toxicology 08/2015; DOI:10.1007/s00204-015-1587-5 · 5.98 Impact Factor
Available from: Ruixin Zhu
- "Chemical substructure-based in silico techniques have been wildly used as an effective and popular approach to reduce the cost in identifying molecules suitable for pharmaceutical development in early stage of drug discovery [28,29]. In our database HIM, substructure search is also available by JChem. "
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Herbal medicine has long been viewed as a valuable asset for potential new drug discovery and herbal ingredients’ metabolites, especially the in vivo metabolites were often found to gain better pharmacological, pharmacokinetic and even better safety profiles compared to their parent compounds. However, these herbal metabolite information is still scattered and waiting to be collected.
HIM database manually collected so far the most comprehensive available in-vivo metabolism information for herbal active ingredients, as well as their corresponding bioactivity, organs and/or tissues distribution, toxicity, ADME and the clinical research profile. Currently HIM contains 361 ingredients and 1104 corresponding in-vivo metabolites from 673 reputable herbs. Tools of structural similarity, substructure search and Lipinski’s Rule of Five are also provided. Various links were made to PubChem, PubMed, TCM-ID (Traditional Chinese Medicine Information database) and HIT (Herbal ingredients’ targets databases).
A curated database HIM is set up for the in vivo metabolites information of the active ingredients for Chinese herbs, together with their corresponding bioactivity, toxicity and ADME profile. HIM is freely accessible to academic researchers at http://www.bioinformatics.org.cn/.
Journal of Cheminformatics 05/2013; 5(1):28. DOI:10.1186/1758-2946-5-28 · 4.55 Impact Factor
Available from: Sean Ekins
- "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.
British Journal of Pharmacology 10/2007; 152(1):9-20. DOI:10.1038/sj.bjp.0707305 · 4.84 Impact Factor
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