Literature DB >> 17533416

Chemogenomic approaches to rational drug design.

D Rognan1.   

Abstract

Paradigms in drug design and discovery are changing at a significant pace. Concomitant to the sequencing of over 180 several genomes, the high-throughput miniaturization of chemical synthesis and biological evaluation of a multiple compounds on gene/protein expression and function opens the way to global drug-discovery approaches, no more focused on a single target but on an entire family of related proteins or on a full metabolic pathway. Chemogenomics is this emerging research field aimed at systematically studying the biological effect of a wide array of small molecular-weight ligands on a wide array of macromolecular targets. Since the quantity of existing data (compounds, targets and assays) and of produced information (gene/protein expression levels and binding constants) are too large for manual manipulation, information technologies play a crucial role in planning, analysing and predicting chemogenomic data. The present review will focus on predictive in silico chemogenomic approaches to foster rational drug design and derive information from the simultaneous biological evaluation of multiple compounds on multiple targets. State-of-the-art methods for navigating in either ligand or target space will be presented and concrete drug design applications will be mentioned.

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Year:  2007        PMID: 17533416      PMCID: PMC1978269          DOI: 10.1038/sj.bjp.0707307

Source DB:  PubMed          Journal:  Br J Pharmacol        ISSN: 0007-1188            Impact factor:   8.739


  88 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Assessing scoring functions for protein-ligand interactions.

Authors:  Philippe Ferrara; Holger Gohlke; Daniel J Price; Gerhard Klebe; Charles L Brooks
Journal:  J Med Chem       Date:  2004-06-03       Impact factor: 7.446

3.  Chemometric analysis of ligand receptor complementarity: identifying Complementary Ligands Based on Receptor Information (CoLiBRI).

Authors:  Scott Oloff; Shuxing Zhang; Nagamani Sukumar; Curt Breneman; Alexander Tropsha
Journal:  J Chem Inf Model       Date:  2006 Mar-Apr       Impact factor: 4.956

4.  Global mapping of pharmacological space.

Authors:  Gaia V Paolini; Richard H B Shapland; Willem P van Hoorn; Jonathan S Mason; Andrew L Hopkins
Journal:  Nat Biotechnol       Date:  2006-07       Impact factor: 54.908

5.  Comparison of protein active site structures for functional annotation of proteins and drug design.

Authors:  Robert Powers; Jennifer C Copeland; Katherine Germer; Kelly A Mercier; Viswanathan Ramanathan; Peter Revesz
Journal:  Proteins       Date:  2006-10-01

6.  Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors.

Authors:  James H Nettles; Jeremy L Jenkins; Andreas Bender; Zhan Deng; John W Davies; Meir Glick
Journal:  J Med Chem       Date:  2006-11-16       Impact factor: 7.446

7.  Efficient detection of three-dimensional structural motifs in biological macromolecules by computer vision techniques.

Authors:  R Nussinov; H J Wolfson
Journal:  Proc Natl Acad Sci U S A       Date:  1991-12-01       Impact factor: 11.205

8.  In silico-guided target identification of a scaffold-focused library: 1,3,5-triazepan-2,6-diones as novel phospholipase A2 inhibitors.

Authors:  Pascal Muller; Gersande Lena; Eric Boilard; Sofiane Bezzine; Gérard Lambeau; Gilles Guichard; Didier Rognan
Journal:  J Med Chem       Date:  2006-11-16       Impact factor: 7.446

9.  GASH: an improved algorithm for maximizing the number of equivalent residues between two protein structures.

Authors:  Daron M Standley; Hiroyuki Toh; Haruki Nakamura
Journal:  BMC Bioinformatics       Date:  2005-09-08       Impact factor: 3.169

10.  S4: structure-based sequence alignments of SCOP superfamilies.

Authors:  James Casbon; Mansoor A S Saqi
Journal:  Nucleic Acids Res       Date:  2005-01-01       Impact factor: 16.971

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  50 in total

Review 1.  Designing antimicrobial peptides: form follows function.

Authors:  Christopher D Fjell; Jan A Hiss; Robert E W Hancock; Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2011-12-16       Impact factor: 84.694

2.  PESDserv: a server for high-throughput comparison of protein binding site surfaces.

Authors:  Sourav Das; Michael P Krein; Curt M Breneman
Journal:  Bioinformatics       Date:  2010-06-10       Impact factor: 6.937

3.  Chemogenomic approaches to drug discovery: similar receptors bind similar ligands.

Authors:  T Klabunde
Journal:  Br J Pharmacol       Date:  2007-05-29       Impact factor: 8.739

Review 4.  Clinical pharmacology--providing tools and expertise for translational medicine.

Authors:  J K Aronson; A Cohen; L D Lewis
Journal:  Br J Clin Pharmacol       Date:  2008-02       Impact factor: 4.335

5.  A structural chemogenomics analysis of aminergic GPCRs: lessons for histamine receptor ligand design.

Authors:  A J Kooistra; S Kuhne; I J P de Esch; R Leurs; C de Graaf
Journal:  Br J Pharmacol       Date:  2013-09       Impact factor: 8.739

6.  Chalcones as a basis for computer-aided drug design: innovative approaches to tackle malaria.

Authors:  Marilia Nn Lima; Bruno J Neves; Gustavo C Cassiano; Marcelo N Gomes; Kaira Cp Tomaz; Leticia T Ferreira; Tatyana A Tavella; Juliana Calit; Daniel Y Bargieri; Eugene N Muratov; Fabio Tm Costa; Carolina Horta Andrade
Journal:  Future Med Chem       Date:  2019-09-26       Impact factor: 3.808

7.  Computer-aided discovery of two novel chalcone-like compounds active and selective against Leishmania infantum.

Authors:  Marcelo N Gomes; Laura M Alcântara; Bruno J Neves; Cleber C Melo-Filho; Lucio H Freitas-Junior; Carolina B Moraes; Rui Ma; Scott G Franzblau; Eugene Muratov; Carolina Horta Andrade
Journal:  Bioorg Med Chem Lett       Date:  2017-04-04       Impact factor: 2.823

8.  Protein-ligand interaction prediction: an improved chemogenomics approach.

Authors:  Laurent Jacob; Jean-Philippe Vert
Journal:  Bioinformatics       Date:  2008-08-01       Impact factor: 6.937

Review 9.  Machine learning for in silico virtual screening and chemical genomics: new strategies.

Authors:  Jean-Philippe Vert; Laurent Jacob
Journal:  Comb Chem High Throughput Screen       Date:  2008-09       Impact factor: 1.339

10.  Analysis of substructural variation in families of enzymatic proteins with applications to protein function prediction.

Authors:  Drew H Bryant; Mark Moll; Brian Y Chen; Viacheslav Y Fofanov; Lydia E Kavraki
Journal:  BMC Bioinformatics       Date:  2010-05-11       Impact factor: 3.169

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