Literature DB >> 15723626

Computer-aided drug design strategies used in the discovery of fructose 1, 6-bisphosphatase inhibitors.

M Rami Reddy1, Mark D Erion.   

Abstract

Computational assessment of the binding affinity of enzyme inhibitors prior to synthesis is an important component of computer-aided drug design (CADD) paradigms. The free energy perturbation (FEP) methodology is the most accurate means of estimating relative binding affinities between two inhibitors. However, due to its complexity and computation-intensive nature, practical applications are restricted to analysis of structurally-related inhibitors. Accordingly, there is a need for methods that enable rapid assessment of a large number of structurally-unrelated molecules in a suitably accurate manner. In this review, the FEP method is compared with molecular mechanics (MM) methods to assess the advantages of each in the estimation of relative binding affinities of inhibitors to an enzyme. Qualitative predictions of relative binding free energies of fructose 1, 6-bisphosphatase inhibitors using MM methods are discussed and compared with the corresponding FEP results. The results indicate that the MM based methods and the FEP method are useful in the qualitative and quantitative assessment of relative binding affinities of enzyme inhibitors, respectively, prior to synthesis.

Mesh:

Substances:

Year:  2005        PMID: 15723626     DOI: 10.2174/1381612053382160

Source DB:  PubMed          Journal:  Curr Pharm Des        ISSN: 1381-6128            Impact factor:   3.116


  3 in total

1.  Homology modeling of 5-lipoxygenase and hints for better inhibitor design.

Authors:  P Aparoy; R N Reddy; Lalitha Guruprasad; M R Reddy; P Reddanna
Journal:  J Comput Aided Mol Des       Date:  2008-01-30       Impact factor: 3.686

Review 2.  Advantages of Structure-Based Drug Design Approaches in Neurological Disorders.

Authors:  Murali Aarthy; Umesh Panwar; Chandrabose Selvaraj; Sanjeev Kumar Singh
Journal:  Curr Neuropharmacol       Date:  2017-11-14       Impact factor: 7.363

3.  Developing and validating predictive decision tree models from mining chemical structural fingerprints and high-throughput screening data in PubChem.

Authors:  Lianyi Han; Yanli Wang; Stephen H Bryant
Journal:  BMC Bioinformatics       Date:  2008-09-25       Impact factor: 3.169

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.