118 related articles for article (PubMed ID: 12653505)
1. Prediction of aqueous solubility of organic compounds based on a 3D structure representation.
Yan A; Gasteiger J
J Chem Inf Comput Sci; 2003; 43(2):429-34. PubMed ID: 12653505
[TBL] [Abstract][Full Text] [Related]
2. Modeling of Gibbs energy of formation of organic compounds by linear and nonlinear methods.
Yan A
J Chem Inf Model; 2006; 46(6):2299-304. PubMed ID: 17125172
[TBL] [Abstract][Full Text] [Related]
3. Prediction of aqueous solubility and partition coefficient optimized by a genetic algorithm based descriptor selection method.
Wegner JK; Zell A
J Chem Inf Comput Sci; 2003; 43(3):1077-84. PubMed ID: 12767167
[TBL] [Abstract][Full Text] [Related]
4. Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods.
Yan A; Gasteiger J; Krug M; Anzali S
J Comput Aided Mol Des; 2004 Feb; 18(2):75-87. PubMed ID: 15287695
[TBL] [Abstract][Full Text] [Related]
5. Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation.
Votano JR; Parham M; Hall LH; Kier LB; Hall LM
Chem Biodivers; 2004 Nov; 1(11):1829-41. PubMed ID: 17191819
[TBL] [Abstract][Full Text] [Related]
6. ADME evaluation in drug discovery. 4. Prediction of aqueous solubility based on atom contribution approach.
Hou TJ; Xia K; Zhang W; Xu XJ
J Chem Inf Comput Sci; 2004; 44(1):266-75. PubMed ID: 14741036
[TBL] [Abstract][Full Text] [Related]
7. Prediction of pKa values for aliphatic carboxylic acids and alcohols with empirical atomic charge descriptors.
Zhang J; Kleinöder T; Gasteiger J
J Chem Inf Model; 2006; 46(6):2256-66. PubMed ID: 17125168
[TBL] [Abstract][Full Text] [Related]
8. Kohonen network study of aromatic compounds based on electronic and nonelectronic structure descriptors.
Panek JJ; Jezierska A; Vracko M
J Chem Inf Model; 2005; 45(2):264-72. PubMed ID: 15807487
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Prediction of soil sorption coefficient of a diverse set of organic chemicals from molecular structure.
Huuskonen J
J Chem Inf Comput Sci; 2003; 43(5):1457-62. PubMed ID: 14502478
[TBL] [Abstract][Full Text] [Related]
11. Prediction of impact sensitivity of nitro energetic compounds by neural network based on electrotopological-state indices.
Wang R; Jiang J; Pan Y; Cao H; Cui Y
J Hazard Mater; 2009 Jul; 166(1):155-86. PubMed ID: 19101083
[TBL] [Abstract][Full Text] [Related]
12. In silico prediction of buffer solubility based on quantum-mechanical and HQSAR- and topology-based descriptors.
Göller AH; Hennemann M; Keldenich J; Clark T
J Chem Inf Model; 2006; 46(2):648-58. PubMed ID: 16562995
[TBL] [Abstract][Full Text] [Related]
13. General melting point prediction based on a diverse compound data set and artificial neural networks.
Karthikeyan M; Glen RC; Bender A
J Chem Inf Model; 2005; 45(3):581-90. PubMed ID: 15921448
[TBL] [Abstract][Full Text] [Related]
14. Quantitative structure-lambda(max) relationship study on flavones by heuristic method and radial basis function neural network.
Liu H; Wen Y; Luan F; Gao Y; Li X
Anal Chim Acta; 2009 Sep; 649(1):52-61. PubMed ID: 19664462
[TBL] [Abstract][Full Text] [Related]
15. Modeling of cyclin-dependent kinase inhibition by 1H-pyrazolo[3,4-d]pyrimidine derivatives using artificial neural network ensembles.
Fernández M; Tundidor-Camba A; Caballero J
J Chem Inf Model; 2005; 45(6):1884-95. PubMed ID: 16309296
[TBL] [Abstract][Full Text] [Related]
16. Anticancer activity of selected phenolic compounds: QSAR studies using ridge regression and neural networks.
Nandi S; Vracko M; Bagchi MC
Chem Biol Drug Des; 2007 Nov; 70(5):424-36. PubMed ID: 17949360
[TBL] [Abstract][Full Text] [Related]
17. Benchmarking of linear and nonlinear approaches for quantitative structure-property relationship studies of metal complexation with ionophores.
Tetko IV; Solov'ev VP; Antonov AV; Yao X; Doucet JP; Fan B; Hoonakker F; Fourches D; Jost P; Lachiche N; Varnek A
J Chem Inf Model; 2006; 46(2):808-19. PubMed ID: 16563012
[TBL] [Abstract][Full Text] [Related]
18. QSAR modeling of anti-invasive activity of organic compounds using structural descriptors.
Katritzky AR; Kuanar M; Dobchev DA; Vanhoecke BW; Karelson M; Parmar VS; Stevens CV; Bracke ME
Bioorg Med Chem; 2006 Oct; 14(20):6933-9. PubMed ID: 16908166
[TBL] [Abstract][Full Text] [Related]
19. Quantitative structure-property relationship study of n-octanol-water partition coefficients of some of diverse drugs using multiple linear regression.
Ghasemi J; Saaidpour S
Anal Chim Acta; 2007 Dec; 604(2):99-106. PubMed ID: 17996529
[TBL] [Abstract][Full Text] [Related]
20. Modeling aqueous solubility.
Butina D; Gola JM
J Chem Inf Comput Sci; 2003; 43(3):837-41. PubMed ID: 12767141
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
