Literature DB >> 19891421

Combinatorial library enumeration and lead hopping using comparative interaction fingerprint analysis and classical 2D QSAR methods for seeking novel GABA(A) alpha(3) modulators.

R S K Vijayan1, Indrani Bera, M Prabu, Sangita Saha, Nanda Ghoshal.   

Abstract

Selective modulators of GABA(A) alpha(3) (gamma amino butyric acid alpha(3)) receptor are known to alleviate the side effects associated with nonspecific modulators. A follow up study was undertaken on a series of functionally selective phthalazines with an ideological credo of identifying more potent isofunctional chemotypes. A bioisosteric database enumerated using the combichem approach endorsed mining in a lead-like chemical space. Primary screening of the massive library was undertaken using the "Miscreen" toolkit, which uses sophisticated bayesian statistics for calculating bioactivity score. The resulting subset, thus, obtained was mined using a novel proteo-chemometric method that integrates molecular docking and QSAR formalism termed CoIFA (comparative interaction fingerprint analysis). CoIFA encodes protein-ligand interaction terms as propensity values based on a statistical inference to construct categorical QSAR models that assist in decision making during virtual screening. In the absence of an experimentally resolved structure of GABA(A) alpha(3) receptor, standard comparative modeling techniques were employed to construct a homology model of GABA(A) alpha(3) receptor. A typical docking study was then carried out on the modeled structure, and the interaction fingerprints generated based on the docked binding mode were used to derive propensity values for the interacting atom pairs that served as pseudo-energy variables to generate a CoIFA model. The classification accuracy of the CoIFA model was validated using different metrics derived from a confusion matrix. Further predictive lead mining was carried out using a consensus two-dimensional QSAR approach, which offers a better predictive protocol compared to the arbitrary choice of a single QSAR model. The predictive ability of the generated model was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. Few analogs designed using the concept of bioisosterism were found to be promising and could be considered for synthesis and subsequent screening.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19891421     DOI: 10.1021/ci900309s

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  4 in total

1.  Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds.

Authors:  Yanmin Zhang; Yu Jiao; Xiao Xiong; Haichun Liu; Ting Ran; Jinxing Xu; Shuai Lu; Anyang Xu; Jing Pan; Xin Qiao; Zhihao Shi; Tao Lu; Yadong Chen
Journal:  Mol Divers       Date:  2015-05-29       Impact factor: 2.943

Review 2.  Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

Authors:  Vertika Gautam; Anand Gaurav; Neeraj Masand; Vannajan Sanghiran Lee; Vaishali M Patil
Journal:  Mol Divers       Date:  2022-07-11       Impact factor: 3.364

3.  Binding affinity prediction with property-encoded shape distribution signatures.

Authors:  Sourav Das; Michael P Krein; Curt M Breneman
Journal:  J Chem Inf Model       Date:  2010-02-22       Impact factor: 4.956

4.  Investigations on inhibitors of hedgehog signal pathway: a quantitative structure-activity relationship study.

Authors:  Ruixin Zhu; Qi Liu; Jian Tang; Huiliang Li; Zhiwei Cao
Journal:  Int J Mol Sci       Date:  2011-05-11       Impact factor: 5.923

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.