Crossref
journal-article
Elsevier BV
Journal of Pharmaceutical Sciences (78)
References
20
Referenced
175
10.1016/S0169-409X(00)00129-0
/ Adv Drug Deliv Rev / Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings by Lipinski (2001)10.1021/jm9810102
/ J Med Chem / Correlation of human jejunal permeability (in vivo) of drugs with experimentally and theoretically derived parameters. A multivariate data analysis approach by Winiwarter (1998)10.1124/dmd.32.1.132
/ Drug Metab Dispos / Development of a computational approach to predict blood–brain barrier permeability by Liu (2004)10.1002/jps.10005
/ J Pharm Sci / Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism‐based prediction of volume of distribution by Poulin (2002)10.1002/jps.1134
/ J Pharm Sci / High throughput physicochemical profiling for drug discovery by Kerns (2001)10.2174/1389557033487638
/ Mini Rev Med Chem / The WWW as a tool to obtain molecular parameters by Tetko (2003)10.2174/1389557033487601
/ Mini Rev Med Chem / Fragmental methods in the analysis of biological activities of diverse compound sets by Japertas (2003)10.2174/1568026033452078
/ Curr Top Med Chem / Theoretical property predictions by Livingstone (2003){'key': '10.1002/jps.20217_bb0050', 'series-title': 'Subsistent constants for correlation analysis in chemistry and biology', 'author': 'Hansch', 'year': '1979'}
/ Subsistent constants for correlation analysis in chemistry and biology by Hansch (1979){'key': '10.1002/jps.20217_bb0055', 'series-title': 'LogP2004, the 3rd lipophilicity symposium', 'first-page': 'L‐22', 'author': 'Lombardo', 'year': '2004'}
/ LogP2004, the 3rd lipophilicity symposium by Lombardo (2004)10.1021/jm049509l
/ J Med Chem / Application of ALOGPS 2.1 to predict LogD distribution coefficient for Pfizer proprietary compounds by Tetko (2004)10.1021/ci025515j
/ J Chem Inf Comput Sci / Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program by Tetko (2002)10.1021/ci010368v
/ J Chem Inf Comput Sci / Prediction of n‐octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E‐state indices by Tetko (2001)10.1021/ci000392t
/ J Chem Inf Comput Sci / Estimation of aqueous solubility of chemical compounds using E‐state indices by Tetko (2001)10.1021/ci010379o
/ J Chem Inf Comput Sci / Neural network studies. 4. Introduction to associative neural networks by Tetko (2002)10.1023/A:1019903710291
/ Neural Proc Lett / Associative neural network by Tetko (2002){'key': '10.1002/jps.20217_bb0090', 'series-title': 'LogP2004, the 3rd lipophilicity symposium', 'first-page': 'C‐30', 'author': 'Tetko', 'year': '2004'}
/ LogP2004, the 3rd lipophilicity symposium by Tetko (2004){'key': '10.1002/jps.20217_bb0095', 'series-title': 'LogP2004, the 3rd lipophilicity symposium', 'first-page': 'C‐17', 'author': 'Tetko', 'year': '2004'}
/ LogP2004, the 3rd lipophilicity symposium by Tetko (2004)10.1021/ci034229k
/ J Chem Inf Comput Sci / The impact of available experimental data on the prediction of (1)h NMR chemical shifts by neural networks by Binev (2004)10.1016/S0960-894X(02)01035-1
/ Bioorg Med Chem Lett / A structure‐permeability study of small drug‐like molecules by Fichert (2003)
Dates
Type | When |
---|---|
Created | 20 years, 9 months ago (Nov. 7, 2004, 1:31 p.m.) |
Deposited | 6 years, 6 months ago (Feb. 1, 2019, 10:21 p.m.) |
Indexed | 1 month ago (July 16, 2025, 9:48 a.m.) |
Issued | 20 years, 8 months ago (Dec. 1, 2004) |
Published | 20 years, 8 months ago (Dec. 1, 2004) |
Published Print | 20 years, 8 months ago (Dec. 1, 2004) |
@article{Tetko_2004, title={Application of ALOGPS to predict 1‐octanol/water distribution coefficients, logP, and logD, of AstraZeneca in‐house database}, volume={93}, ISSN={0022-3549}, url={http://dx.doi.org/10.1002/jps.20217}, DOI={10.1002/jps.20217}, number={12}, journal={Journal of Pharmaceutical Sciences}, publisher={Elsevier BV}, author={Tetko, Igor V. and Bruneau, Pierre}, year={2004}, month=dec, pages={3103–3110} }