BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

108 related articles for article (PubMed ID: 20728966)

  • 21. Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses.
    Louis B; Agrawal VK; Khadikar PV
    Eur J Med Chem; 2010 Sep; 45(9):4018-25. PubMed ID: 20584562
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Linear and nonlinear quantitative structure-property relationship models for solubility of some anthraquinone, anthrone and xanthone derivatives in supercritical carbon dioxide.
    Hemmateenejad B; Shamsipur M; Miri R; Elyasi M; Foroghinia F; Sharghi H
    Anal Chim Acta; 2008 Mar; 610(1):25-34. PubMed ID: 18267136
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Quantitative study of the structure-retention index relationship in the imine family.
    Acevedo-Martínez J; Escalona-Arranz JC; Villar-Rojas A; Téllez-Palmero F; Pérez-Rosés R; González L; Carrasco-Velar R
    J Chromatogr A; 2006 Jan; 1102(1-2):238-44. PubMed ID: 16288769
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Benchmarking of QSAR models for blood-brain barrier permeation.
    Konovalov DA; Coomans D; Deconinck E; Heyden YV
    J Chem Inf Model; 2007; 47(4):1648-56. PubMed ID: 17602606
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Prediction of micelle-water partition coefficient from the theoretical derived molecular descriptors.
    Fatemi MH; Karimian F
    J Colloid Interface Sci; 2007 Oct; 314(2):665-72. PubMed ID: 17673243
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Prediction of HPLC retention index using artificial neural networks and IGroup E-state indices.
    Albaugh DR; Hall LM; Hill DW; Kertesz TM; Parham M; Hall LH; Grant DF
    J Chem Inf Model; 2009 Apr; 49(4):788-99. PubMed ID: 19309176
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Prediction of cytotoxicity data (CC(50)) of anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives by artificial neural network trained with Levenberg-Marquardt algorithm.
    Arab Chamjangali M; Beglari M; Bagherian G
    J Mol Graph Model; 2007 Jul; 26(1):360-7. PubMed ID: 17350867
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Quantitative structure-activity relationship study of serotonin (5-HT7) receptor inhibitors using modified ant colony algorithm and adaptive neuro-fuzzy interference system (ANFIS).
    Jalali-Heravi M; Asadollahi-Baboli M
    Eur J Med Chem; 2009 Apr; 44(4):1463-70. PubMed ID: 19013691
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Synthesis, antimicrobial, and QSAR studies of substituted benzamides.
    Kumar A; Narasimhan B; Kumar D
    Bioorg Med Chem; 2007 Jun; 15(12):4113-24. PubMed ID: 17428669
    [TBL] [Abstract][Full Text] [Related]  

  • 30. A quantitative structure property relationship for prediction of solubilization of hazardous compounds using GA-based MLR in CTAB micellar media.
    Ghasemi JB; Abdolmaleki A; Mandoumi N
    J Hazard Mater; 2009 Jan; 161(1):74-80. PubMed ID: 18456399
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Combination of artificial neural network technique and linear free energy relationship parameters in the prediction of gradient retention times in liquid chromatography.
    Fatemi MH; Abraham MH; Poole CF
    J Chromatogr A; 2008 May; 1190(1-2):241-52. PubMed ID: 18395736
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide.
    Tabaraki R; Khayamian T; Ensafi AA
    J Mol Graph Model; 2006 Sep; 25(1):46-54. PubMed ID: 16337156
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds.
    Prado-Prado FJ; González-Díaz H; de la Vega OM; Ubeira FM; Chou KC
    Bioorg Med Chem; 2008 Jun; 16(11):5871-80. PubMed ID: 18485714
    [TBL] [Abstract][Full Text] [Related]  

  • 34. The development of dopamine D2-receptor selective antagonists.
    Högberg T
    Drug Des Discov; 1993; 9(3-4):333-50. PubMed ID: 8400011
    [TBL] [Abstract][Full Text] [Related]  

  • 35. External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean.
    Schüürmann G; Ebert RU; Chen J; Wang B; Kühne R
    J Chem Inf Model; 2008 Nov; 48(11):2140-5. PubMed ID: 18954136
    [TBL] [Abstract][Full Text] [Related]  

  • 36. A 3D QSAR study on a set of dopamine D4 receptor antagonists.
    Boström J; Böhm M; Gundertofte K; Klebe G
    J Chem Inf Comput Sci; 2003; 43(3):1020-7. PubMed ID: 12767161
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Use of computer-assisted methods for the modeling of the retention time of a variety of volatile organic compounds: a PCA-MLR-ANN approach.
    Jalali-Heravi M; Kyani A
    J Chem Inf Comput Sci; 2004; 44(4):1328-35. PubMed ID: 15272841
    [TBL] [Abstract][Full Text] [Related]  

  • 38. In silico prediction of dermal penetration rate of chemicals from their molecular structural descriptors.
    Fatemi MH; Malekzadeh H
    Environ Toxicol Pharmacol; 2012 Sep; 34(2):297-306. PubMed ID: 22659232
    [TBL] [Abstract][Full Text] [Related]  

  • 39. QSAR Studying of Oxidation Behavior of Benzoxazines as an Important Pharmaceutical Property.
    Baher E; Darzi N
    Iran J Pharm Res; 2017; 16(1):146-157. PubMed ID: 28496470
    [TBL] [Abstract][Full Text] [Related]  

  • 40. A new hybrid system of QSAR models for predicting bioconcentration factors (BCF).
    Zhao C; Boriani E; Chana A; Roncaglioni A; Benfenati E
    Chemosphere; 2008 Dec; 73(11):1701-7. PubMed ID: 18954891
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 6.