Literature DB >> 9667851

Modern computational chemistry and drug discovery: structure generating programs.

R S Bohacek1, C McMartin.   

Abstract

During 1996 and 1997, the first reports were disclosed of active enzyme inhibitors based entirely on novel structures created by de novo methods. De novo methods have also been used to modify and significantly improve the binding affinity of an HIV protease inhibitor. Work continues in the improvement of methods for the de novo design of compounds which fit and chemically complement a binding site. De novo algorithms that generate only synthetically feasible structures have also been reported. In addition, methods are being developed for the automatic computer generation of virtual molecular libraries which can be searched to identify molecules to match a pharmacophore or fit into a binding site.

Mesh:

Year:  1997        PMID: 9667851     DOI: 10.1016/s1367-5931(97)80004-x

Source DB:  PubMed          Journal:  Curr Opin Chem Biol        ISSN: 1367-5931            Impact factor:   8.822


  5 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.  Designing the molecular future.

Authors:  Gisbert Schneider
Journal:  J Comput Aided Mol Des       Date:  2011-11-30       Impact factor: 3.686

3.  Minimal pharmacophoric elements and fragment hopping, an approach directed at molecular diversity and isozyme selectivity. Design of selective neuronal nitric oxide synthase inhibitors.

Authors:  Haitao Ji; Benjamin Z Stanton; Jotaro Igarashi; Huiying Li; Pavel Martásek; Linda J Roman; Thomas L Poulos; Richard B Silverman
Journal:  J Am Chem Soc       Date:  2008-03-06       Impact factor: 15.419

Review 4.  Targeting UDP-galactopyranose mutases from eukaryotic human pathogens.

Authors:  Karina Kizjakina; John J Tanner; Pablo Sobrado
Journal:  Curr Pharm Des       Date:  2013       Impact factor: 3.116

5.  KVFinder: steered identification of protein cavities as a PyMOL plugin.

Authors:  Saulo H P Oliveira; Felipe A N Ferraz; Rodrigo V Honorato; José Xavier-Neto; Tiago J P Sobreira; Paulo S L de Oliveira
Journal:  BMC Bioinformatics       Date:  2014-06-17       Impact factor: 3.169

  5 in total

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