PMID- 27477467
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20160802
LR  - 20160801
IS  - 1868-1743 (Print)
IS  - 1868-1743 (Linking)
VI  - 31
IP  - 6-7
DP  - 2012 Jul
TI  - QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to 
      Bubble Point Temperatures of Binary Mixtures of Liquids.
PG  - 491-502
LID - 10.1002/minf.201200006 [doi]
AB  - This paper is devoted to the development of methodology for QSPR modeling of 
      mixtures and its application to vapor/liquid equilibrium diagrams for bubble 
      point temperatures of binary liquid mixtures. Two types of special mixture 
      descriptors based on SiRMS and ISIDA approaches were developed. SiRMS-based 
      fragment descriptors involve atoms belonging to both components of the mixture, 
      whereas the ISIDA fragments belong only to one of these components. The models 
      were built on the data set containing the phase diagrams for 167 mixtures 
      represented by different combinations of 67 pure liquids. Consensus models were 
      developed using nonlinear Support Vector Machine (SVM), Associative Neural 
      Networks (ASNN), and Random Forest (RF) approaches. For SVM and ASNN 
      calculations, the ISIDA fragment descriptors were used, whereas Simplex 
      descriptors were employed in RF models. The models have been validated using 
      three different protocols: "Points out", "Mixtures out" and "Compounds out", 
      based on the specific rules to form training/test sets in each fold of 
      cross-validation. A final validation of the models has been performed on an 
      additional set of 94 mixtures represented by combinations of novel 34 compounds 
      and modeling set chemicals with each other. The root mean squared error of 
      predictions for new mixtures of already known liquids does not exceed 5.7 K, 
      which outperforms COSMO-RS models. Developed QSAR methodology can be applied to 
      the modeling of any nonadditive property of binary mixtures (antiviral 
      activities, drug formulation, etc.).
CI  - Copyright (c) 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
FAU - Oprisiu, I
AU  - Oprisiu I
AD  - University of Strasbourg, Strasbourg, France phone: +33.3.68.65.15.60.
AD  - Processium, 62 Boulevard Niels Bohr, BP 2132, F 69603 Villeurbanne, France.
FAU - Varlamova, E
AU  - Varlamova E
AD  - A. V. Bogatsky Physical-Chemical Institute, Odessa, Ukraine.
FAU - Muratov, E
AU  - Muratov E
AD  - A. V. Bogatsky Physical-Chemical Institute, Odessa, Ukraine.
AD  - University of North Carolina, Chapel Hill, USA.
FAU - Artemenko, A
AU  - Artemenko A
AD  - A. V. Bogatsky Physical-Chemical Institute, Odessa, Ukraine.
FAU - Marcou, G
AU  - Marcou G
AD  - University of Strasbourg, Strasbourg, France phone: +33.3.68.65.15.60.
FAU - Polishchuk, P
AU  - Polishchuk P
AD  - A. V. Bogatsky Physical-Chemical Institute, Odessa, Ukraine.
FAU - Kuz'min, V
AU  - Kuz'min V
AD  - A. V. Bogatsky Physical-Chemical Institute, Odessa, Ukraine.
FAU - Varnek, A
AU  - Varnek A
AD  - University of Strasbourg, Strasbourg, France phone: +33.3.68.65.15.60. 
      varnek@chimie.u-strasbg.fr.
LA  - eng
PT  - Journal Article
DEP - 20120706
PL  - Germany
TA  - Mol Inform
JT  - Molecular informatics
JID - 101529315
OTO - NOTNLM
OT  - Bubble point curve
OT  - Mixtures prediction
OT  - QSAR/QSPR
OT  - Vapor/liquid equilibrium
EDAT- 2012/07/01 00:00
MHDA- 2012/07/01 00:01
CRDT- 2016/08/02 06:00
PHST- 2012/01/16 00:00 [received]
PHST- 2012/04/23 00:00 [accepted]
PHST- 2016/08/02 06:00 [entrez]
PHST- 2012/07/01 00:00 [pubmed]
PHST- 2012/07/01 00:01 [medline]
AID - 10.1002/minf.201200006 [doi]
PST - ppublish
SO  - Mol Inform. 2012 Jul;31(6-7):491-502. doi: 10.1002/minf.201200006. Epub 2012 Jul 
      6.