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Journal of Chemical Information and Computer Sciences|August 24, 2000
Neural network modeling for estimation of partition coefficient based on atom-type electrotopological state indicesHuuskonen, Livingstone, TetkoJournal of Pharmaceutical Sciences|February 9, 1999
Prediction of partition coefficient based on atom-type electrotopological state indicesJ J Huuskonen, A E Villa, I V TetkoEuropean Journal of Medicinal Chemistry|March 15, 2001
Prediction of aqueous solubility for a diverse set of organic compounds based on atom-type electrotopological state indicesJ Huuskonen, J Rantanen, D LivingstoneJournal of Computer-Aided Molecular Design|March 1, 1997
Data modelling with neural networks: advantages and limitationsD J Livingstone, D T Manallack, I V TetkoJournal of Chemical Information and Computer Sciences|July 1, 1996
Neural network studies. 2. Variable selectionI V Tetko, A E Villa, D J LivingstoneSAR and QSAR in Environmental Research|May 20, 2008
Prediction of drug solubility from molecular structure using a drug-like training setJ Huuskonen, D J Livingstone, D T ManallackJournal of Medicinal Chemistry|July 13, 2001
Volume learning algorithm artificial neural networks for 3D QSAR studiesI V Tetko, V V Kovalishyn, D J LivingstoneJournal of Computer-Aided Molecular Design|November 23, 2001
Simultaneous prediction of aqueous solubility and octanol/water partition coefficient based on descriptors derived from molecular structureD J Livingstone, M G Ford, J J Huuskonen, et al.Bioorganicheskaia Khimiia|September 18, 2001
[Application of neural networks using the volume learning algorithm for the study of structure-activity relationship in chemical compounds]V V Kovalishin, I V Tetko, A I Luĭk, et al.Journal of Chemical Information and Computer Sciences|June 13, 2000
Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topologyHuuskonenPageof 306