Literature DB >> 15068363

Development and testing of a de novo drug-design algorithm.

Eric Pellegrini1, Martin J Field.   

Abstract

In this article we present an implementation of a de novo drug-design algorithm. The algorithm starts with a molecule placed in the binding site of a protein and then modifies it using a sequential growth approach. This involves successive cycles of suppression of randomly picked groups in the molecule and their replacement by other groups chosen from databanks of linear or cyclic fragments. The algorithm has been coupled with the DYNAMO library which allows the simulation of macromolecules using molecular mechanical and quantum chemical methods. The main body of the article describes the methodologies we use to create, characterize and evaluate putative ligands. We also consider briefly an application of the algorithm to a protein of pharmacological interest, the neuraminidase of the influenza virus, and discuss the strengths and weaknesses of our approach.

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Year:  2003        PMID: 15068363     DOI: 10.1023/b:jcam.0000017362.66268.d5

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  22 in total

1.  De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks.

Authors:  G Schneider; M L Lee; M Stahl; P Schneider
Journal:  J Comput Aided Mol Des       Date:  2000-07       Impact factor: 3.686

2.  The Protein Data Bank.

Authors:  Helen M Berman; Tammy Battistuz; T N Bhat; Wolfgang F Bluhm; Philip E Bourne; Kyle Burkhardt; Zukang Feng; Gary L Gilliland; Lisa Iype; Shri Jain; Phoebe Fagan; Jessica Marvin; David Padilla; Veerasamy Ravichandran; Bohdan Schneider; Narmada Thanki; Helge Weissig; John D Westbrook; Christine Zardecki
Journal:  Acta Crystallogr D Biol Crystallogr       Date:  2002-05-29

3.  Computer design of bioactive molecules: a method for receptor-based de novo ligand design.

Authors:  J B Moon; W J Howe
Journal:  Proteins       Date:  1991

4.  RECAP--retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry.

Authors:  X Q Lewell; D B Judd; S P Watson; M M Hann
Journal:  J Chem Inf Comput Sci       Date:  1998 May-Jun

5.  Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes.

Authors:  M D Eldridge; C W Murray; T R Auton; G V Paolini; R P Mee
Journal:  J Comput Aided Mol Des       Date:  1997-09       Impact factor: 3.686

6.  Development and validation of a genetic algorithm for flexible docking.

Authors:  G Jones; P Willett; R C Glen; A R Leach; R Taylor
Journal:  J Mol Biol       Date:  1997-04-04       Impact factor: 5.469

7.  Flexible ligand docking using a genetic algorithm.

Authors:  C M Oshiro; I D Kuntz; J S Dixon
Journal:  J Comput Aided Mol Des       Date:  1995-04       Impact factor: 3.686

8.  Ligand docking to proteins with discrete side-chain flexibility.

Authors:  A R Leach
Journal:  J Mol Biol       Date:  1994-01-07       Impact factor: 5.469

Review 9.  Protein structure--based drug design.

Authors:  P J Whittle; T L Blundell
Journal:  Annu Rev Biophys Biomol Struct       Date:  1994

10.  SPROUT: recent developments in the de novo design of molecules.

Authors:  V J Gillet; W Newell; P Mata; G Myatt; S Sike; Z Zsoldos; A P Johnson
Journal:  J Chem Inf Comput Sci       Date:  1994 Jan-Feb
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  2 in total

1.  The concept of template-based de novo design from drug-derived molecular fragments and its application to TAR RNA.

Authors:  Andreas Schüller; Marcel Suhartono; Uli Fechner; Yusuf Tanrikulu; Sven Breitung; Ute Scheffer; Michael W Göbel; Gisbert Schneider
Journal:  J Comput Aided Mol Des       Date:  2007-12-07       Impact factor: 3.686

2.  Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets.

Authors:  William J Allen; Brian C Fochtman; Trent E Balius; Robert C Rizzo
Journal:  J Comput Chem       Date:  2017-09-22       Impact factor: 3.376

  2 in total

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