Literature DB >> 18537680

Recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design.

Qi-Shi Du1, Ri-Bo Huang, Kuo-Chen Chou.   

Abstract

This review is to summarize three new QSAR (quantitative structure-activity relationship) methods recently developed in our group and their applications for drug design. Based on more solid theoretical models and advanced mathematical techniques, the conventional QSAR technique has been recast in the following three aspects. (1) In the fragment-based two dimensional QSAR, or abbreviated as FB-QSAR, the molecular structures in a family of drug candidates are divided into several fragments according to the substitutes being investigated. The bioactivities of drug candidates are correlated with physicochemical properties of the molecular fragments through two sets of coefficients: one is for the physicochemical properties and the other for the molecular fragments. (2) In the multiple field three dimensional QSAR, or MF-3D-QSAR, more molecular potential fields are integrated into the comparative molecular field analysis (CoMFA) through two sets of coefficients: one is for the potential fields and the other for the Cartesian three dimensional grid points. (3) In the AABPP (amino acid-based peptide prediction), the bioactivities of peptides or proteins are correlated with the physicochemical properties of all or partial residues of the sequence through two sets of coefficients: one is for the physicochemical properties of amino acids and the other for the weight factors of the residues. Meanwhile, an iterative double least square (IDLS) technique is developed for solving the two sets of coefficients in a training dataset alternately and iteratively. Using the two sets of coefficients, one can predict the bioactivity of a query peptide, protein, or drug candidate. Compared with the old methods, the new QSAR approaches as summarized in this review possess machine learning ability, can remarkably enhance the prediction power, and provide more structural information. Meanwhile, the future challenge and possible development in this area have been briefly addressed as well.

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Year:  2008        PMID: 18537680     DOI: 10.2174/138920308784534005

Source DB:  PubMed          Journal:  Curr Protein Pept Sci        ISSN: 1389-2037            Impact factor:   3.272


  21 in total

1.  QSAR classification of metabolic activation of chemicals into covalently reactive species.

Authors:  Chin Yee Liew; Chuen Pan; Andre Tan; Ke Xin Magneline Ang; Chun Wei Yap
Journal:  Mol Divers       Date:  2012-02-28       Impact factor: 2.943

2.  Consensus models for CDK5 inhibitors in silico and their application to inhibitor discovery.

Authors:  Jiansong Fang; Ranyao Yang; Li Gao; Shengqian Yang; Xiaocong Pang; Chao Li; Yangyang He; Ai-Lin Liu; Guan-Hua Du
Journal:  Mol Divers       Date:  2014-12-16       Impact factor: 2.943

3.  Study of peptide fingerprints of parasite proteins and drug-DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks.

Authors:  Lázaro Guillermo Pérez-Montoto; María Auxiliadora Dea-Ayuela; Francisco J Prado-Prado; Francisco Bolas-Fernández; Florencio M Ubeira; Humberto González-Díaz
Journal:  Polymer (Guildf)       Date:  2009-06-03       Impact factor: 4.430

4.  Comparative docking study of anibamine as the first natural product CCR5 antagonist in CCR5 homology models.

Authors:  Guo Li; Kendra M Haney; Glen E Kellogg; Yan Zhang
Journal:  J Chem Inf Model       Date:  2009-01       Impact factor: 4.956

5.  Benchmarking ligand-based virtual High-Throughput Screening with the PubChem database.

Authors:  Mariusz Butkiewicz; Edward W Lowe; Ralf Mueller; Jeffrey L Mendenhall; Pedro L Teixeira; C David Weaver; Jens Meiler
Journal:  Molecules       Date:  2013-01-08       Impact factor: 4.411

Review 6.  Reviewing ligand-based rational drug design: the search for an ATP synthase inhibitor.

Authors:  Chia-Hsien Lee; Hsuan-Cheng Huang; Hsueh-Fen Juan
Journal:  Int J Mol Sci       Date:  2011-08-17       Impact factor: 5.923

7.  An FPGA implementation to detect selective cationic antibacterial peptides.

Authors:  Carlos Polanco González; Marco Aurelio Nuño Maganda; Miguel Arias-Estrada; Gabriel del Rio
Journal:  PLoS One       Date:  2011-06-28       Impact factor: 3.240

8.  Predicting biological functions of compounds based on chemical-chemical interactions.

Authors:  Le-Le Hu; Chen Chen; Tao Huang; Yu-Dong Cai; Kuo-Chen Chou
Journal:  PLoS One       Date:  2011-12-29       Impact factor: 3.240

9.  3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors.

Authors:  Mahreen Arooj; Sundarapandian Thangapandian; Shalini John; Swan Hwang; Jong Keun Park; Keun Woo Lee
Journal:  Int J Mol Sci       Date:  2011-12-12       Impact factor: 5.923

10.  DDESC: Dragon database for exploration of sodium channels in human.

Authors:  Sunil Sagar; Mandeep Kaur; Adam Dawe; Sundararajan Vijayaraghava Seshadri; Alan Christoffels; Ulf Schaefer; Aleksandar Radovanovic; Vladimir B Bajic
Journal:  BMC Genomics       Date:  2008-12-20       Impact factor: 3.969

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