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Journal Abstract Search

120 related items for PubMed ID: 30826336

  • 1. Aug-MIA-QSAR based strategy in bioactivity prediction of a series of flavonoid derivatives as HIV-1 inhibitors.
    Muthukumaran P, Rajiniraja M.
    J Theor Biol; 2019 May 21; 469():18-24. PubMed ID: 30826336
    [Abstract] [Full Text] [Related]

  • 2. MIA-QSAR based model for bioactivity prediction of flavonoid derivatives as acetylcholinesterase inhibitors.
    Muthukumaran P, Rajiniraja M.
    J Theor Biol; 2018 Dec 14; 459():103-110. PubMed ID: 30267791
    [Abstract] [Full Text] [Related]

  • 3. aug-MIA-QSAR modeling of antimicrobial activities and design of multi-target anilide derivatives.
    Nunes CA, Freitas MP.
    J Microbiol Methods; 2013 Sep 14; 94(3):217-20. PubMed ID: 23831437
    [Abstract] [Full Text] [Related]

  • 4. MIA-QSAR evaluation of a series of sulfonylurea herbicides.
    Bitencourt M, Freitas MP.
    Pest Manag Sci; 2008 Aug 14; 64(8):800-7. PubMed ID: 18338340
    [Abstract] [Full Text] [Related]

  • 5. Exploring MIA-QSARs' for antimalarial quinolon-4(1H)-imines.
    Duarte MH, Barigye SJ, Freitas MP.
    Comb Chem High Throughput Screen; 2015 Aug 14; 18(2):208-16. PubMed ID: 25543687
    [Abstract] [Full Text] [Related]

  • 6. Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method.
    Wei Y, Li W, Du T, Hong Z, Lin J.
    Int J Mol Sci; 2019 Jul 22; 20(14):. PubMed ID: 31336592
    [Abstract] [Full Text] [Related]

  • 7. Non-Linear Quantitative Structure⁻Activity Relationships Modelling, Mechanistic Study and In-Silico Design of Flavonoids as Potent Antioxidants.
    Žuvela P, David J, Yang X, Huang D, Wong MW.
    Int J Mol Sci; 2019 May 10; 20(9):. PubMed ID: 31083440
    [Abstract] [Full Text] [Related]

  • 8. Structure-activity relationships for the anti-HIV activity of flavonoids.
    Olivero-Verbel J, Pacheco-Londoño L.
    J Chem Inf Comput Sci; 2002 May 10; 42(5):1241-6. PubMed ID: 12377014
    [Abstract] [Full Text] [Related]

  • 9. Rational Design of Colchicine Derivatives as anti-HIV Agents via QSAR and Molecular Docking.
    Worachartcheewan A, Songtawee N, Siriwong S, Prachayasittikul S, Nantasenamat C, Prachayasittikul V.
    Med Chem; 2019 May 10; 15(4):328-340. PubMed ID: 30251609
    [Abstract] [Full Text] [Related]

  • 10. MIA-QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives.
    Goodarzi M, Freitas MP.
    Eur J Med Chem; 2010 Apr 10; 45(4):1352-8. PubMed ID: 20060625
    [Abstract] [Full Text] [Related]

  • 11. MIA-QSAR modelling of anti-HIV-1 activities of some 2-amino-6-arylsulfonylbenzonitriles and their thio and sulfinyl congeners.
    Freitas MP.
    Org Biomol Chem; 2006 Mar 21; 4(6):1154-9. PubMed ID: 16525561
    [Abstract] [Full Text] [Related]

  • 12. Linear and non-linear quantitative structure-activity relationship models on indole substitution patterns as inhibitors of HIV-1 attachment.
    Nirouei M, Ghasemi G, Abdolmaleki P, Tavakoli A, Shariati S.
    Indian J Biochem Biophys; 2012 Jun 21; 49(3):202-10. PubMed ID: 22803336
    [Abstract] [Full Text] [Related]

  • 13. A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.
    Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O.
    Mol Divers; 2006 Aug 21; 10(3):405-14. PubMed ID: 16896545
    [Abstract] [Full Text] [Related]

  • 14. Bi- and multilinear PLS coupled to MIA-QSAR in the prediction of antifungal activities of some benzothiazole derivatives.
    Bitencourt M, Freitas MP.
    Med Chem; 2009 Jan 21; 5(1):79-86. PubMed ID: 19149653
    [Abstract] [Full Text] [Related]

  • 15. MIA-QSAR coupled to different regression methods for the modeling of antimalarial activities of 2-aziridinyl and 2,3-bis-(aziridinyl)-1,4-naphtoquinonyl sulfate and acylate derivatives.
    Goodarzi M, Freitas MP.
    Med Chem; 2011 Nov 21; 7(6):645-54. PubMed ID: 22313304
    [Abstract] [Full Text] [Related]

  • 16. Development of linear and nonlinear predictive QSAR models and their external validation using molecular similarity principle for anti-HIV indolyl aryl sulfones.
    Roy K, Mandal AS.
    J Enzyme Inhib Med Chem; 2008 Dec 21; 23(6):980-95. PubMed ID: 18608761
    [Abstract] [Full Text] [Related]

  • 17. Four-dimensional structure-activity relationship model to predict HIV-1 integrase strand transfer inhibition using LQTA-QSAR methodology.
    de Melo EB, Ferreira MM.
    J Chem Inf Model; 2012 Jul 23; 52(7):1722-32. PubMed ID: 22657398
    [Abstract] [Full Text] [Related]

  • 18. ANN QSAR workflow for predicting the inhibition of HIV-1 reverse transcriptase by pyridinone non-nucleoside derivatives.
    Barzegar A, Zamani-Gharehchamani E, Kadkhodaie-Ilkhchi A.
    Future Med Chem; 2017 Jul 23; 9(11):1175-1191. PubMed ID: 28722475
    [Abstract] [Full Text] [Related]

  • 19. Comparative QSAR based on neural networks for the anti-HIV activity of HEPT derivatives.
    Douali L, Villemin D, Cherqaoui D.
    Curr Pharm Des; 2003 Jul 23; 9(22):1817-26. PubMed ID: 12871199
    [Abstract] [Full Text] [Related]

  • 20. Introducing new dimensions in MIA-QSAR: a case for chemokine receptor inhibitors.
    Nunes CA, Freitas MP.
    Eur J Med Chem; 2013 Apr 23; 62():297-300. PubMed ID: 23357311
    [Abstract] [Full Text] [Related]

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