R. Todeschini, V. Consonni, and C. , Comparative Molecular Field Analysis (CoMFA) of MX Compounds using different Semi-empirical Methods: LUMO Field and its Correlation with Mutagenic Activity Rational design of potent sialidase-based inhibitors of influenza virus replication, Quantitative Structure-Activity Relationships, pp.189-193, 1993.

A. Varnek and V. P. Solov-'ev, “In Silico” Design of Potential Anti-HIV Actives Using Fragment Descriptors, Combinatorial Chemistry & High Throughput Screening, vol.8, issue.5, pp.403-416, 2005.
DOI : 10.2174/1386207054546513

A. Varnek, ISIDA -Platform for virtual screening based on fragment and pharmacophoric descriptors. Current Computer-Aided Drug Design, pp.191-198, 2008.

V. P. Solov-'ev, A. Varnek, and G. Wipff, Modeling of Ion Complexation and Extraction Using Substructural Molecular Fragments, Journal of Chemical Information and Computer Sciences, vol.40, issue.3, pp.847-858, 2000.
DOI : 10.1021/ci9901340

F. Ruggiu, ISIDA Property-Labelled Fragment Descriptors, Molecular Informatics, vol.35, issue.12, pp.29-855, 2010.
DOI : 10.1002/minf.201000099

A. Varnek, ISIDA -Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors. Cur. Computer-Aided Drug Design Stochastic versus stepwise strategies for quantitative structure -Activity relationship generation -How much effort may the mining for successful QSAR models take, Journal of Chemical Information and Modeling, vol.10, issue.433, pp.191-198, 2007.

V. P. Solov-'ev and A. Varnek, Structure-Property Modeling of Metal Binders Using Molecular Fragments, Rus. Chem. Bull, issue.7, pp.53-1434, 2004.

A. Varnek, Exhaustive QSPR studies of a large diverse set of ionic liquids: How accurately can we predict melting points, Journal of Chemical Information and Modeling, issue.3, pp.47-1111, 2007.

A. L. Boulesteix and K. Strimmer, Partial least squares: a versatile tool for the analysis of high-dimensional genomic data, Briefings in Bioinformatics, vol.8, issue.1, pp.32-44, 2007.
DOI : 10.1093/bib/bbl016

N. V. Artemenko, Artificial neural network and fragmental approach in prediction of physicochemical properties of organic compounds, Russian Chemical Bulletin, vol.52, issue.1, pp.20-29, 2003.
DOI : 10.1023/A:1022467508832

I. V. Tetko, Neural Network Studies. 4. Introduction to Associative Neural Networks, Journal of Chemical Information and Computer Sciences, vol.42, issue.3, pp.717-728, 2002.
DOI : 10.1021/ci010379o

V. N. Vapnik and K. R. Muller, Statistical Learning Theory An introduction to kernel-based learning algorithms, IEEE Trans Neural Netw, vol.17, issue.122, pp.181-201, 1998.

C. Chang and C. Lin, LIBSVM, Machine Learning, pp.1-27, 2001.
DOI : 10.1145/1961189.1961199

Q. Première-partie-méthodologie, A. K. Seewald, A. Tropsha, P. Gramatica, and V. K. Gombar, How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models, Proceedings of the Nineteenth International Conference on Machine Learning, pp.554-561, 2002.

I. I. Baskin, N. Kireeva, and A. Varnek, The One-Class Classification Approach to Data Description and to Models Applicability Domain, Molecular Informatics, vol.47, issue.8-9, pp.29-37, 2010.
DOI : 10.1002/minf.201000063

B. E. Poling, J. M. Prausnitz, and J. P. , The properties of gases and liquids, 2001.

J. Schwartzentruber and . Thermodynamique, Available from: http://nte.mines-albi, 2011.

N. A. Gokcen, Gibbs-duhem-margules laws, Journal of Phase Equilibria, vol.16, issue.1, pp.50-51, 1996.
DOI : 10.1007/BF02648369

URL : http://doi.org/10.1007/bf02648369

A. Fredenslund, R. L. Jones, and J. M. Prausnitz, Group-contribution estimation of activity coefficients in nonideal liquid mixtures, AIChE Journal, vol.21, issue.6, pp.1086-1099, 1975.
DOI : 10.1002/aic.690210607

R. Wittig, J. Lohmann, and J. Gmehling, Vapor???Liquid Equilibria by UNIFAC Group Contribution. 6. Revision and Extension, Industrial & Engineering Chemistry Research, vol.42, issue.1, pp.183-188, 2002.
DOI : 10.1021/ie020506l

A. Jakob, Further Development of Modified UNIFAC (Dortmund):?? Revision and Extension 5, Industrial & Engineering Chemistry Research, vol.45, issue.23, pp.45-7924, 2006.
DOI : 10.1021/ie060355c

A. Klamt, F. Eckert, and C. -. , COSMO-RS: a novel and efficient method for the a priori prediction of thermophysical data of liquids, Fluid Phase Equilibria, vol.172, issue.1, pp.43-72, 2000.
DOI : 10.1016/S0378-3812(00)00357-5

A. Klamt, J. Cosmo-rs-u, and . Gmehling, A novel tool for the prediction of industrially relevant thermodynamic data Available from: http://www.ensic.inplnancy.fr, Modified Unifac Model .1. Prediction of Vle, He, and Gamma-Infinity. Industrial & Engineering Chemistry Research, pp.26-1372, 1987.

P. Gierycz and B. Wisniewska, Applicability of the unifac method for prediction of binary and ternary vapour-liquid equilibrium data in systems formed by hydrocarbons and alcohols, 11. Siimer, E. and L. Kudryavtseva, Correlation and Prediction of Excess- Enthalpies of Ester Plus N-Alkane Systems Using the Unifac Model. Thermochimica Acta, pp.269-276, 1990.
DOI : 10.1016/0040-6031(90)80546-B

D. Geana and V. Feroiu, Prediction of Vapor???Liquid Equilibria at Low and High Pressures from UNIFAC Activity Coefficients at Infinite Dilution, Industrial & Engineering Chemistry Research, vol.37, issue.3, pp.1173-1180, 1998.
DOI : 10.1021/ie970472v

O. Spuhl and W. Arlt, COSMO???RS Predictions in Chemical EngineeringA Study of the Applicability to Binary VLE, Industrial & Engineering Chemistry Research, vol.43, issue.4, pp.852-861, 2004.
DOI : 10.1021/ie034009w

Z. Guo, Predictions of flavonoid solubility in ionic liquids by COSMO-RS: experimental verification, structural elucidation, and solvation characterization, Green Chemistry, vol.42, issue.1, pp.1362-1373, 2007.
DOI : 10.1039/b709786g

S. Roy, Density and molar volume predictions using COSMO-RS for ionic liquids. An approach to solvent design Thermodynamic Analysis of Systems Formed by Alkyl Esters with alpha,omega-Alkyl Dibromides: New Experimental Information and the 1, Predictions of thermodynamic properties of energetic materials using COSMO-RS. Iccs 2010 -International Conference on Computational Science, Proceedings Development and current status of the Korea Thermophysical Properties Databank (KDB), pp.1197-1205, 2001.

L. Horsley and H. , Table of Azeotropes and Nonazeotropes, in AZEOTROPIC DATA, pp.1-314, 1973.

A. J. Gordon and R. A. Ford, The ?hemist's Companion. A Handbook of Practical Data, Techniques, and References, 1972.

J. Gmehling, R. Boelts, ]. J. Gmehling, and R. Bçlts, Azeotropic Data for Binary and Ternary Systems at Moderate Pressures, Journal of Chemical & Engineering Data, vol.41, issue.2, pp.202-209, 1996.
DOI : 10.1021/je950228f

U. Weidlich and J. Gmehling, A modified UNIFAC model. 1. Prediction of VLE, hE, and .gamma..infin., Industrial & Engineering Chemistry Research, vol.26, issue.7, pp.1372-1381, 1987.
DOI : 10.1021/ie00067a018

K. Tochigi, D. Tiegs, J. Gmehling, and K. Kojima, Determination of new ASOG parameters., JOURNAL OF CHEMICAL ENGINEERING OF JAPAN, vol.23, issue.4, pp.453-463, 1990.
DOI : 10.1252/jcej.23.453

T. F. Anderson and J. M. Prausnitz, Application of the UNIQUAC Equation to Calculation of Multicomponent Phase Equilibria. 1. Vapor-Liquid Equilibria, Proc. Dd, pp.552-561, 1978.
DOI : 10.1021/i260068a028

A. Fredenslund, J. Gmehling, M. L. Michelsen, P. Rasmussen, and J. M. Prausnitz, Computerized Design of Multicomponent Distillation Columns Using the UNIFAC Group Contribution Method for Calculation of Activity Coefficients, Proc. Dd, pp.450-462, 1977.
DOI : 10.1021/i260064a004

Z. Lei, J. Zhang, Q. Li, and B. Chen, UNIFAC Model for Ionic Liquids, Industrial & Engineering Chemistry Research, vol.48, issue.5, pp.2697-2704, 2009.
DOI : 10.1021/ie801496e

A. Klamt, Conductor-like Screening Model for Real Solvents: A New Approach to the Quantitative Calculation of Solvation Phenomena, The Journal of Physical Chemistry, vol.99, issue.7, pp.2224-2235, 1995.
DOI : 10.1021/j100007a062

F. Eckert, COSMOtherm User's Manual, C2.1 Release 01, 2010.

T. Mu, J. Rarey, and J. Gmehling, Performance of COSMO-RS with Sigma Profiles from Different Model Chemistries, Industrial & Engineering Chemistry Research, vol.46, issue.20, pp.6612-6629, 2007.
DOI : 10.1021/ie0702126

A. Klamt, F. Eckert, and W. Arlt, COSMO-RS: An Alternative to Simulation for Calculating Thermodynamic Properties of Liquid Mixtures, Annual Review of Chemical and Biomolecular Engineering, vol.1, issue.1, pp.101-122, 2010.
DOI : 10.1146/annurev-chembioeng-073009-100903

E. N. Muratov, E. V. Varlamova, A. G. Artemenko, T. Khristova, V. E. Kuz-'min et al., QSAR analysis of [(biphenyloxy)propyl]isoxazoles: agents against coxsackievirus B3, Future Medicinal Chemistry, vol.3, issue.1, pp.15-27, 2011.
DOI : 10.4155/fmc.10.278

A. Kravtsov, P. Karpov, I. Baskin, V. Palyulin, and N. Zefirov, ???Bimolecular??? QSPR: Estimation of the solvation free energy of organic molecules in different solvents, Doklady Chemistry, vol.414, issue.1, pp.128-131, 2007.
DOI : 10.1134/S0012500807050072

A. Kravtsov, P. Karpov, I. Baskin, V. Palyulin, and N. Zefirov, Prediction of rate constants of S N 2 reactions by the multicomponent QSPR method, Doklady Chemistry, vol.440, issue.2, pp.299-301, 2011.
DOI : 10.1134/S0012500811100107

A. Kravtsov, P. Karpov, I. Baskin, V. Palyulin, and N. Zefirov, Prediction of the preferable mechanism of nucleophilic substitution at saturated carbon atom and prognosis of S N 1 rate constants by means of QSPR, Doklady Chemistry, vol.441, issue.1, pp.314-317, 2011.
DOI : 10.1134/S0012500811110048

S. Ajmani, S. C. Rogers, M. H. Barley, and D. J. Livingstone, Application of QSPR to Mixtures, Journal of Chemical Information and Modeling, vol.46, issue.5, pp.2043-2055, 2006.
DOI : 10.1021/ci050559o

S. Ajmani, S. C. Rogers, M. H. Barley, A. N. Burgess, and D. J. Livingstone, Characterization of Mixtures Part 1: Prediction of Infinite-Dilution Activity Coefficients Using Neural Network-Based QSPR Models, QSAR & Combinatorial Science, vol.43, issue.11-12, pp.1346-1361, 2008.
DOI : 10.1002/qsar.200860022

S. Ajmani, S. C. Rogers, M. H. Barley, A. N. Burgess, and D. J. Livingstone, Characterization of Mixtures. Part 2: QSPR Models for Prediction of Excess Molar Volume and Liquid Density Using Neural Networks, Molecular Informatics, vol.40, issue.8-9, pp.645-653, 2010.
DOI : 10.1002/minf.201000027

A. R. Katritzky, I. B. Stoyanova-slavova, K. Tämm, T. Tamm, and M. Karelson, Application of the QSPR Approach to the Boiling Points of Azeotropes, The Journal of Physical Chemistry A, vol.115, issue.15, pp.3475-3479, 2011.
DOI : 10.1021/jp104287p

N. Alpert and P. J. Elvinq, Vapor-Liquid Equilibria in Binary Systems Systems Involving cis- or trans-Dichloroethylene and an Alcohol, Industrial & Engineering Chemistry, vol.43, issue.5, pp.1174-1177, 1951.
DOI : 10.1021/ie50497a050

K. J. Miller and H. Huang, Vapor-liquid equilibrium for binary systems 2-butanone with 2-butanol, 1-pentanol, and isoamyl alcohol, Journal of Chemical & Engineering Data, vol.17, issue.1, pp.77-78, 1972.
DOI : 10.1021/je60052a026

T. Hiaki, K. Yamato, and K. Kojima, Vapor-liquid equilibria of 2,3-dimethylbutane + methanol or ethanol at 101.3 kPa, Journal of Chemical & Engineering Data, vol.37, issue.2, pp.203-206, 1992.
DOI : 10.1021/je00006a017

C. E. Kirby and M. Van-winkle, Vapor-liquid equilibriums: 2,3-dimethylbutane-methanol and 2,3-dimethylbutane-methanol-chloroform systems, Journal of Chemical & Engineering Data, vol.15, issue.1, pp.177-182, 1970.
DOI : 10.1021/je60044a017

I. V. Tetko, The Prediction of Physicochemical Properties in Computational Toxicology, pp.240-275, 2006.

E. N. Muratov, A. G. Artemenko, E. V. Varlamova, P. G. Polischuk, V. P. Lozitsky et al., : application of Simplex QSAR approach in antiviral research, Future Medicinal Chemistry, vol.2, issue.7, pp.1205-1226, 2010.
DOI : 10.4155/fmc.10.194

V. E. Kuz-'min, A. G. Artemenko, E. N. Muratov, I. L. Volineckaya, V. A. Makarov et al., Quantitative Structure???Activity Relationship Studies of [(Biphenyloxy)propyl]isoxazole Derivatives. Inhibitors of Human Rhinovirus 2 Replication, Journal of Medicinal Chemistry, vol.50, issue.17, pp.4205-4213, 2007.
DOI : 10.1021/jm0704806

P. G. Polishchuk, E. N. Muratov, A. G. Artemenko, O. G. Kolumbin, N. N. Muratov et al., Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity, Journal of Chemical Information and Modeling, vol.49, issue.11, pp.2481-2488, 2009.
DOI : 10.1021/ci900203n

L. Breiman, The Wadsworth Statistics/Probability Series, p.358, 1984.

V. Svetnik, A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan et al., Random Forest:??? A Classification and Regression Tool for Compound Classification and QSAR Modeling, Journal of Chemical Information and Computer Sciences, vol.43, issue.6, pp.1947-58, 2003.
DOI : 10.1021/ci034160g

K. R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, An introduction to kernel-based learning algorithms, IEEE Transactions on Neural Networks, vol.12, issue.2, pp.181-201, 2001.
DOI : 10.1109/72.914517

J. Wisniak and A. Tamir, Vapor-liquid equilibria at 760 mmHg in the system methanol-2-propanol-propyl bromide and its binaries, Journal of Chemical & Engineering Data, vol.30, issue.3, pp.339-344, 1985.
DOI : 10.1021/je00041a032

A. Chinikamala, Vapor-liquid equilibriums of binary systems containing selected hydrocarbons with perfluorobenzene, Journal of Chemical & Engineering Data, vol.18, issue.3, pp.322-325, 1973.
DOI : 10.1021/je60058a007

R. 1. Kang and J. W. , Development and current status of the Korea Thermophysical Properties Databank (KDB), International Journal of Thermophysics, vol.22, issue.2, pp.487-494, 2001.
DOI : 10.1023/A:1010726915591

. Rdevelopmentcoreteam, R: A language and environment for statistical computing 2004, R Foundation for Statistical Computing

B. H. Mevik and R. Wehrens, The pls package: Principal component and partial least squares regression in R, Journal of Statistical Software, issue.2, p.18, 2007.

A. Klamt and F. Eckert, COSMOtherm, a powerful tool for the calculation of solvation effects and phase equilibria. Abstracts of Papers of the, pp.234-234, 2000.

A. K. Seewald, How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness, Proceedings of the Nineteenth International Conference on Machine Learning, pp.554-561, 2002.

M. A. Stadtherr, R. W. Maier, and J. F. Brennecke, Reliable computation of homogeneous azeotropes, Aiche Journal, issue.8, pp.44-1745, 1998.

R. Kearfott, Interval Newton/generalized bisection when there are singularities near roots, Annals of Operations Research, vol.22, issue.6, pp.181-196, 1990.
DOI : 10.1007/BF02283694

E. Salomone and J. Espinosa, Prediction of Homogeneous Azeotropes with Interval Analysis Techniques Exploiting Topological Considerations, Industrial & Engineering Chemistry Research, vol.40, issue.6, pp.1580-1588, 2001.
DOI : 10.1021/ie000608g

W. T. Zharov, L. A. Serafinov, and M. Q. , Physico-chemical Fundamentals of Distillation and Recification Prediction of Homogeneous Azeotropes by the UNIFAC Method for Binary Refrigerant Mixtures, Journal of Chemical and Engineering Data, issue.1, pp.55-52, 1975.

J. Lee and H. Kim, Development of a criterion for azeotrope prediction of binary refrigerant mixtures, Korean Journal of Chemical Engineering, vol.19, issue.4, pp.863-865, 2002.
DOI : 10.1007/BF02706980

J. H. Hildebrand, The Term, Nature, issue.4281, pp.168-868, 1951.

G. Schembecker and K. H. Simmrock, AZEOPERT - A heuristic-numeric system for the prediction of azeotrope formation, Computers & Chemical Engineering, vol.19, pp.253-258, 1995.
DOI : 10.1016/0098-1354(95)87045-8

Y. J. Kim and K. H. Simmrock, AZEOPERT: An expert system for the prediction of azeotrope formation???I. Binary azeotropes, Computers & Chemical Engineering, vol.21, issue.1, pp.93-111, 1997.
DOI : 10.1016/0098-1354(95)00249-9

Y. J. Kim and S. K. Kang, Prediction of the types of binary azeotropes in an expert system, AZEOPERT, Journal of Industrial and Engineering Chemistry, vol.5, issue.2, pp.105-115, 1999.

C. A. Nascimento, R. M. Alves, and F. H. Quina, New approach for the prediction of azeotropy in binary systems, Computers & Chemical Engineering, issue.12, pp.27-1755, 2003.

J. W. Kang, Development and current status of the Korea Thermophysical Properties Databank (KDB), International Journal of Thermophysics, vol.22, issue.2, pp.487-494, 2001.
DOI : 10.1023/A:1010726915591

L. Horsley and H. , Table of Azeotropes and Nonazeotropes, in AZEOTROPIC DATA, pp.1-314, 1973.

A. Jakob, Further Development of Modified UNIFAC (Dortmund):?? Revision and Extension 5, Industrial & Engineering Chemistry Research, vol.45, issue.23, pp.45-7924, 2006.
DOI : 10.1021/ie060355c

C. Rucker, G. Rucker, and M. Meringer, y-Randomization and Its Variants in QSPR/QSAR, Journal of Chemical Information and Modeling, vol.47, issue.6, pp.47-2345, 2007.
DOI : 10.1021/ci700157b

J. D. Van-der-waals, O. Over-de-constinuiteit-van-den-gas-en-vloeistoftoestand-redlich, J. N. Kwong, D. Peng, D. B. Robinson et al., Doctoral Dissertation. 1873 On the thermodynamics of solutions; an equation of state; fugacities of gaseous solutions A New Two-Constant Equation of State Vapor-Liquid Equilibrium. XI. A New Expression for the Excess Free Energy of Mixing Local compositions in thermodynamic excess functions for liquid mixtures Application of Uniquac Equation to Calculation of Multicomponent Phase-Equilibria 0.1. Vapor-Liquid- Equilibria, Industrial & Engineering Chemistry Research ARTICLE Proc. Des. Dev) Gmehling, J.; Rasmussen, P.; Fredenslund, A. A Survey of the Calculation of Phase-Equilibria with the Aid of the Unifac-Method, pp.233-244, 1949.

C. Prausnitz, J. M. Tavares, F. W. Punnathanam, S. Monson, and P. A. , Thermodynamics of fluid-phase equilibria for standard chemical engineering operations Crystal nucleation in binary hard sphere mixtures: A Monte Carlo simulation study, 11) Salomone, E.; Espinosa, J. Prediction of Homogeneous Azeotropes with Interval Analysis Techniques Exploiting Topological Considerations, pp.724-733, 1980.

Y. Zhang, J. Liu, J. Wu, R. W. Maier, J. F. Brennecke et al., Prediction of Homogeneous Azeotropes by the UNIFAC Method for Binary Refrigerant Mixtures Reliable computation of reactive azeotropes Reliable Computation of Homogeneous Azeotropes Locating All Homogeneous Azeotropes in Multicomponent Mixtures, Ind. Eng. Chem. Res J. Chem. Eng. Data Comput. Chem. Eng. AIChE J. Ind. Eng. Chem. Res. J, vol.40, issue.36116, pp.1580-1588, 1997.

J. M. Prausnitz, Z. G. Lei, J. G. Zhang, Q. S. Li, B. H. Chen et al., Computerized Design of Multicomponent Distillation- Columns Using Unifac Group Contribution Method for Calculation of Activity-Coefficients UNIFAC Model for Ionic Liquids, Proc. Des. Dev, pp.450-462, 1977.

D. J. Livingstone, S. Ajmani, S. Rogers, and A. N. Burgess, Characterization of Mixtures Part 1: Prediction of Infinite-Dilution Activity Coefficients Using Neural Network-Based QSPR Models, QSAR Comb. Sci, vol.27, issue.11À12, pp.1346-1361, 2008.

D. J. Livingstone, S. Ajmani, S. C. Rogers, M. H. Barley, D. J. Livingstone et al., Characterization of Mixtures Part 2: QSPR Models for Prediction of Excess Molar Volume and Liquid Density Using Neural Networks Application of QSPR to mixtures, Mol. Inform. 2010 J. Chem. Inf. Model, vol.2920, issue.465, pp.645-653, 2006.

M. Karelson, Application of the QSPR Approach to the Boiling Points of Azeotropes, 22) Solov'ev, V. P.; Varnek, A.; Wipff, G. Modeling of Ion Complexation and Extraction Using Substructural Molecular Fragments, pp.3475-3479, 2011.

A. Varnek, A. Tropsha, M. N. Swamy, K. Thulasiraman, . Graphs et al., The Chemist's Companion. A Handbook of Practical Data, Techniques, and References, Chemoinformatic Approaches to Virtual Screening 1À43. (25), pp.537-564, 1972.

T. Suzuki, Structure-Property Modelling of Complex Formation of Strontium with Organic Ligands in Water, J. Struct. Chem, vol.47, issue.2, pp.298-311, 2006.

V. P. Solov-'ev and A. Varnek, Quantitative Structure-Property Relationship Modeling of beta-Cyclodextrin Complexation Free Energies, J. Chem

P. Vayer, V. Solov-'ev, F. Hoonakker, I. V. Tetko, and G. Marcou, ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors, 30) Varnek, A.; Solov'ev, V. P. 00 In Silico 00 Design of Potential, pp.191-198, 2008.

D. Horvath, F. Bonachera, V. Solov-'ev, C. Gaudin, A. Varnek et al., Stochastic versus Stepwise Strategies for Quantitative Structure-Activity Relationship Generation-How Much Effort May the Mining for Successful QSAR Models Take? (32) Solov'ev, V. P.; Varnek, A. Structure-Property Modeling of Metal Binders Using Molecular Fragments, HIV Actives Using Fragment Descriptors. Comb. Chem. High Throughput Screening, pp.403-416, 2004.

G. H. Golub, C. Reinsch, P. H. Muller, P. Neumann, R. Storm et al., Singular Value Decomposition and Least Squares Solutions Tafeln der mathematischen Statistik, 37) Tetko, I. V.; Solov'ev, V. P.; Antonov, pp.1111-1122, 1970.
DOI : 10.1007/978-3-642-86940-2_10

L. H. Horsley, J. Rarey, and D. Ramjugernath, Tables of Azeotropes and Nonazeotropes, in Azeotropic Data-III Estimation of the vapour pressure of nonelectrolyte organic compounds via group contributions and group interactions, 1À613. (40) Ponton, pp.52-63, 1973.

R. Ceriani, R. Gani, A. J. Nannoolal, Y. , J. Rarey et al., Prediction of heat capacities and heats of vaporization of organic liquids by group contribution methods Estimation of pure component properties Part 4: Estimation of the saturated liquid viscosity of non-electrolyte organic compounds via group contributions and group interactions Estimation of pure component properties. Part 2. Estimation of critical property data by group contribution An empirical analysis of research topic query processing, Fluid Phase Equilibria Fluid Phase Equilibria Fluid Phase Equilibria Journal of Chemical Information and Modeling, vol.2837, issue.46, pp.49-55, 2006.

A. R. Katritzky, Structurally Diverse Quantitative Structure???Property Relationship Correlations of Technologically Relevant Physical Properties, Journal of Chemical Information and Computer Sciences, vol.40, issue.1, pp.1-18, 2000.
DOI : 10.1021/ci9903206

A. A. Toropov, Using the maximal topological distance matrix for QSPR modeling of the boiling points of cyclic hydrocarbons 169-172. [9] Tsygankova, I.G., Combination of fragmental and topological descriptors for QSPR estimations of boiling temperature, J. Struct. Chem. Qsar & Combinatorial Science, vol.40, issue.23, pp.629-636, 1999.

A. T. Balaban, Correlations between chemical structure and normal boiling points of halogenated alkanes C1-C4, Journal of Chemical Information and Modeling, vol.32, issue.3, pp.233-237, 1992.
DOI : 10.1021/ci00007a010

C. Rucker, M. Meringer, and A. Kerber, QSPR Using MOLGEN-QSPR:?? The Challenge of Fluoroalkane Boiling Points, Journal of Chemical Information and Modeling, vol.45, issue.1, pp.74-80, 2005.
DOI : 10.1021/ci0497298

A. A. Toropov, Testing the atomic orbital graph as a basis for QSPR modeling of the boiling points of haloalkanes, Journal of Structural Chemistry, vol.34, issue.No. 5, pp.40-950, 1999.
DOI : 10.1007/BF02700704

A. P. Bunz, B. Braun, and R. Janowsky, Application of Quantitative Structure???Performance Relationship and Neural Network Models for the Prediction of Physical Properties from Molecular Structure, Industrial & Engineering Chemistry Research, vol.37, issue.8, pp.3043-3051, 1998.
DOI : 10.1021/ie970910y

M. Kompany-zareh, A QSPR study of boiling point of saturated alcohols using genetic algorithm, Acta Chimica Slovenica, vol.50, pp.259-273, 2003.

K. Roy and A. Saha, QSPR with TAU indices: Boiling points of sulfides and thiols, Indian Journal of Chemistry Section a-Inorganic Bio-Inorganic Physical Theoretical, Analytical Chemistry, vol.43, pp.1369-1376, 2004.

A. J. Chalk, T. Beck, and . Clark, A Quantum Mechanical/Neural Net Model for Boiling Points with Error Estimation, Journal of Chemical Information and Computer Sciences, vol.41, issue.2, pp.457-462, 2001.
DOI : 10.1021/ci0004614

K. G. Joback and R. C. Reid, ESTIMATION OF PURE-COMPONENT PROPERTIES FROM GROUP-CONTRIBUTIONS, Chemical Engineering Communications, vol.4, issue.1-6, pp.233-243, 1987.
DOI : 10.1080/00986448708960487

L. Constantinou and R. Gani, New group contribution method for estimating properties of pure compounds, AIChE Journal, vol.40, issue.10, pp.1697-1710, 1994.
DOI : 10.1002/aic.690401011

S. E. Stein and R. L. Brown, Estimation of normal boiling points from group contributions, Journal of Chemical Information and Modeling, vol.34, issue.3, pp.581-587, 1994.
DOI : 10.1021/ci00019a016

J. Marrero-morejon and E. Pardillo-fontdevila, Estimation of pure compound properties using group-interaction contributions, AIChE Journal, vol.11, issue.3, pp.615-621, 1999.
DOI : 10.1002/aic.690450318

Y. Nannoolal, Estimation of pure component properties, Fluid Phase Equilibria, vol.226, pp.45-63, 2004.
DOI : 10.1016/j.fluid.2004.09.001

D. Ericksen, Use of the DIPPR Database for Development of QSPR Correlations:??? Normal Boiling Point, Journal of Chemical & Engineering Data, vol.47, issue.5, pp.1293-1302, 2002.
DOI : 10.1021/je0255372

L. M. Egolf, M. D. Wessel, and P. C. Jurs, Prediction of boiling points and critical temperatures of industrially important organic compounds from molecular structure, Journal of Chemical Information and Modeling, vol.34, issue.4, pp.947-956, 1994.
DOI : 10.1021/ci00020a032

A. R. Katritzky, V. S. Lobanov, and M. Karelson, Normal Boiling Points for Organic Compounds:??? Correlation and Prediction by a Quantitative Structure???Property Relationship, Journal of Chemical Information and Computer Sciences, vol.38, issue.1, pp.28-41, 1998.
DOI : 10.1021/ci970029v

N. V. Artemenko, Prediction of physical properties of organic compounds using artificial neural networks within the substructure approach, Doklady Chemistry, vol.381, issue.1/3, pp.317-320, 2001.
DOI : 10.1023/A:1012976623974

U. Onken, J. Rareynies, and J. Gmehling, The Dortmund Data Bank: A computerized system for retrieval, correlation, and prediction of thermodynamic properties of mixtures, International Journal of Thermophysics, vol.39, issue.3, pp.739-747, 1989.
DOI : 10.1007/BF00507993

G. H. Golub, M. Heath, and G. Wahba, Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter, Technometrics, vol.5, issue.2, pp.215-223, 1979.
DOI : 10.1080/03610927508827223

V. Trimble, Crc Handbook of Chemistry and Physics -Weast, Rc, Scientist, vol.1, pp.19-19, 1987.

N. , W. De-chimie, and N. , See: http://webbook.nist.gov/chemistry/. [35] Pesticide Properties DataBase, 2011.

S. E. Stein and R. L. Brown, Estimation of normal boiling points from group contributions, Journal of Chemical Information and Modeling, vol.34, issue.3, pp.581-587, 1994.
DOI : 10.1021/ci00019a016

I. V. Tetko, The Prediction of Physicochemical Properties, pp.240-275, 2006.
DOI : 10.1002/9780470145890.ch9

N. V. Artemenko, Prediction of Physical Properties of Organic Compounds Using Artificial Neural Networks within the Substructure Approach, Doklady Chemistry, vol.381, issue.1/3, pp.317-320, 2001.
DOI : 10.1023/A:1012976623974

F. Bonachera and D. Horvath, ChemInform Abstract: Fuzzy Tricentric Pharmacophore Fingerprints. Part 2. Application of Topological Fuzzy Pharmacophore Triplets in Quantitative Structure-Activity Relationships., ChemInform, vol.48, issue.21, pp.409-425, 2008.
DOI : 10.1002/chin.200821219

I. V. Tetko, Associative Neural Network, Neural Processing Letters, vol.16, pp.187-199, 2002.
DOI : 10.1007/978-1-60327-101-1_10

A. Varnek, ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors, Current Computer Aided-Drug Design, vol.4, issue.3, pp.191-198, 2008.
DOI : 10.2174/157340908785747465

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res, vol.3, pp.1157-1182, 2003.

F. E. Grubbs, Procedures for Detecting Outlying Observations in Samples, Technometrics, vol.6, issue.7, pp.1-21, 1969.
DOI : 10.1080/00401706.1969.10490657

D. Horvath, G. Marcou, and A. Varnek, Predicting the Predictability: A Unified Approach to the Applicability Domain Problem of QSAR Models, Journal of Chemical Information and Modeling, vol.49, pp.1762-1776, 2009.

E. F. Srour and T. L. Horvath, Identification of a novel murine CD45 negative bone marrow-derived cell population with in vivo marrow repopulating potential following hematopoietic specification in vitro, Blood, vol.106, pp.490-490, 2005.

J. Bi and K. P. Bennett, Regression Error Characteristic Curves, Proceedings of the 20th International Conference on Machine Learning, pp.45-50, 2003.

J. Marrero and R. Gani, Group-contribution based estimation of pure component properties, Fluid Phase Equilibria, pp.183-184, 2001.

I. V. Tetko, K. V. Balakin, and N. P. Savchuk, In silico approaches to prediction of aqueous and DMSO solubility of drug-like compounds: Trends, problems and solutions, Current Medicinal Chemistry, vol.7, issue.132, pp.223-241, 2006.

Y. Q. Ran and S. H. Yalkowsky, Prediction of Drug Solubility by the General Solubility Equation (GSE), Journal of Chemical Information and Computer Sciences, vol.41, issue.2, pp.41-354, 2001.
DOI : 10.1021/ci000338c

G. Klopman and H. Zhu, Estimation of the Aqueous Solubility of Organic Molecules by the Group Contribution Approach, Journal of Chemical Information and Computer Sciences, vol.41, issue.2, pp.41-439, 2001.
DOI : 10.1021/ci000152d

J. Huuskonen, Estimation of Aqueous Solubility for a Diverse Set of Organic Compounds Based on Molecular Topology, Journal of Chemical Information and Computer Sciences, vol.40, issue.3, pp.773-777, 2000.
DOI : 10.1021/ci9901338

I. V. Tetko, Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices, Journal of Chemical Information and Computer Sciences, vol.41, issue.6, pp.41-1488, 2001.
DOI : 10.1021/ci000392t

J. R. Votano, Prediction of Aqueous Solubility Based on Large Datasets Using Several QSPR Models Utilizing Topological Structure Representation, Chemistry & Biodiversity, vol.8, issue.11, pp.1829-1841, 2004.
DOI : 10.1002/cbdv.200490137

D. Fourches, Modèles multiples en QSAR/QSPR: Développement de nouvelles approches et leurs applications au design "in silico" de nouveaux extractants de métaux, aux propriétés ADMETox ainsi qu'à différentes activités biologiques de molécules organiques, 2007.

Y. Cohen, A fuzzy ARTMAP based on quantitative structure-property relationships (QSPRs) for predicting aqueous solubility of organic compounds, Journal of Chemical Information and Computer Sciences, issue.5, pp.41-1177, 2001.

P. C. Jurs, N. R. Mcelroy-bonachera, and F. , Prediction of aqueous solubility of heteroatomcontaining organic compounds from molecular structure Fuzzy tricentric pharmacophore fingerprints. 1. Topological fuzzy pharmacophore triplets and adapted molecular similarity scoring schemes Predicting the Predictability: A Unified Approach to the Applicability Domain Problem of QSAR Models, Solubility Challenge: Can You Predict Solubilities of 32 Molecules Using a Database of 100 Reliable Measurements? Journal of Chemical Information and Modeling, pp.41-1237, 2001.

G. Oprisiu, D. Marcou, A. Horvath, and . Varnek, 3 Communications par affiche I Predictive models for aqueous solubility based on the ISIDA descriptors, Journées Nationales de la Chémoinformatique, 2009.

. Varnek, Predictive QSPR models for Bubble Point curve of binary liquid mixtures, Summer School on Chemoinformatics, 2010.

I. Oprisiu, G. Marcou, F. Rivollet, D. Horvath, and A. Varnek, Predictive QSPR models for the azeotropic behavior of binary liquid mixtures, 2010.

A. Kuz-'min and . Varnek, QSPR Analysis of Boiling Temperatures of 2-Component Systems, MACC4, 2011.