Literature DB >> 12850335

Chemical substructures in drug discovery.

Cédric Merlot1, Daniel Domine, Christophe Cleva, Dennis J Church.   

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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Year:  2003        PMID: 12850335     DOI: 10.1016/s1359-6446(03)02740-5

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  7 in total

1.  High-throughput pharmacokinetic method: cassette dosing in mice associated with minuscule serial bleedings and LC/MS/MS analysis.

Authors:  Takaho Watanabe; Daniela Schulz; Christophe Morisseau; Bruce D Hammock
Journal:  Anal Chim Acta       Date:  2006-02-10       Impact factor: 6.558

2.  SVM approach for predicting LogP.

Authors:  Quan Liao; Jianhua Yao; Shengang Yuan
Journal:  Mol Divers       Date:  2006-09-22       Impact factor: 2.943

Review 3.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

4.  Distribution of randomly generated activity class characteristic substructures in diverse active and database compounds.

Authors:  José Batista; Jürgen Bajorath
Journal:  Mol Divers       Date:  2008-05-28       Impact factor: 2.943

5.  Stat3 regulates centrosome clustering in cancer cells via Stathmin/PLK1.

Authors:  Edward J Morris; Eiko Kawamura; Jordan A Gillespie; Aruna Balgi; Nagarajan Kannan; William J Muller; Michel Roberge; Shoukat Dedhar
Journal:  Nat Commun       Date:  2017-05-05       Impact factor: 14.919

6.  HIM-herbal ingredients in-vivo metabolism database.

Authors:  Hong Kang; Kailin Tang; Qi Liu; Yi Sun; Qi Huang; Ruixin Zhu; Jun Gao; Duanfeng Zhang; Chenggang Huang; Zhiwei Cao
Journal:  J Cheminform       Date:  2013-05-31       Impact factor: 5.514

7.  Consensus-Based Pharmacophore Mapping for New Set of N-(disubstituted-phenyl)-3-hydroxyl-naphthalene-2-carboxamides.

Authors:  Andrzej Bak; Jiri Kos; Hana Michnova; Tomas Gonec; Sarka Pospisilova; Violetta Kozik; Alois Cizek; Adam Smolinski; Josef Jampilek
Journal:  Int J Mol Sci       Date:  2020-09-09       Impact factor: 5.923

  7 in total

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