Computational Model For Chromatographic Relative Retention Time of Polychlorinated Biphenyls Using Sub-structural Molecular Fragments
Department of Chemistry, Faculty of Science Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
∗E-mail: sasaaidpour@iausdj.ac.ir
Received:
Received: 10 February 2016; revised: 17 March 2016; accepted: 17 March 2016; published online: 24 March 2016
DOI: 10.12921/cmst.2016.22.01.004
Abstract:
Quantitative structure-retention relationship (QSRR) analysis is a useful technique capable of relating chromato- graphic retention time to the chemical structure of a solute. Using the sub-structural molecular fragments (SMF) derived directly from the molecular structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were calculated. An eight-variable regression equation with the correlation coefficient of 0.9945 and the root mean square errors of 0.0134 was developed. Forward and backward stepwise regression variable selection and multi-linear regression analysis (MLRA) are combined to describe the effect of molecular structure on the RRT of PCB according to the QSRR method. To quantitatively relate RRT with the molecular structure MLR analysis is performed on the set of 163 sub-structural molecular fragments (SMF) provided by the ISIDA software. The eight fragments selected by variable subset selection, all belonging to the sub-fragments, adequately represent the structural factors influencing the affinity of PCB to SE-54 stationary phase in the separation process. Finally, a QSRR model is selected based on leave-one-out cross-validation and its prediction ability is further tested on 42 representative compounds excluded from model calibration. The prediction results from the MLR model are in good agreement with the experimental values. By applying the MLR method we can predict the test set with squared cross validated correlation coefficient (Q2ext) of 0.9913 and root mean square error (RMSE) of 0.0169.
Key words:
MLR, polychlorinated biphenyl, QSRR, relative retention time, sub-structural molecular fragment
References:
[1] C.J. Halsall, R.G.M. Lee, P.J. Coleman, et al., PCBs in UK urban air, Environ. Sci. Technol., 29, 2368-2376 (1995).
[2] R.F.Herrick,D.J.LefkowitzandG.A.Weymouth,Soilcon- tamination from PCB containing buildings, Environ. Health Persp., 115, 173-175 (2007).
[3] S. Bayen, E. Koroleva, H.K. Lee, et al., Persistent organic pollutants and heavy metals in typical sea foods consumed in Singapore, J. Toxicol. Environ. Health, Part A 68, 151-166 (2005).
[4] M.P.Simmonds,K.Haraguchi,T.Endo,etal.,Humanhealth significance of organochlorine and mercury contaminants in Japanese whale meat, J. Toxicol. Environ. Health, Part A, 65, 1211-1235(2002).
[5] D.G. Wang, M. Yang, H.L. Jia, et al., Levels, distributions and profiles of polychlorinated biphenyls in surface soils of Dalian, China, Chemosphere, 73, 38-42 (2008).
[6] S. Ohta, H. Tokusawa, T. Nakao, et al., Global contamina- tion of coplanar polybrominated/chlorinated biphenyls (Co- PXBs) in the market fishes from Japan, Chemosphere, 73, 31-38 (2008).
[7] J. She, A. Holden, T.L. Adelsbach, et al., Concentrations and time trends of polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) in aquatic bird eggs from San Francisco Bay, CA 2000-2003, Chemosphere, 73, 201-209 (2008).
[8] A.S. Souza, J.P.M. Torres, R.O. Meire, et al., Organochlo- rine pesticides (OCs) and polychlorinated biphenyls (PCBs) in sediments and crabs (Chasmagnathus granulata, Dana, 1851) from mangroves of Guanabara bay, Rio de Janeiro state, Brazil, Chemosphere, 73, 186-192 (2008).
[9] C.Mori,H.Fukata,K.Sakurai,etal.,Strongcorrelationbe- tween the concentration of dioxins and total PCBs in current Japanese people, Chemosphere, 73, 235-238 (2008).
[10] Y. Masuda, A. Schecter, and O. Papke, Concentrations of PCBs, PCDFs and PCDDs in the blood of Yusho patients and their toxic equivalent contribution, Chemosphere, 37, 1773-1780 (1998).
[11] S.L. Schantz, Developmental neurotoxicity of PCBs in hu- mans: what do we know and where do we go from here?, Neurotoxicol Teratol 18, 217-227 (1996).
[12] M.D.Erickson,AnalyticalchemistryofPCBs,2ndedn.CRC Press LLC, Boca Raton, FL, USA 1997.
[13] E.A. Castro, A. A. Toropov, A.I. Nesterova ,et al., QSPR modeling aqueous solubility of polychlorinated biphenyls by optimization of correlation weights of local and global graph invariants, Central European Journal of Chemistry, 2(3), 500-523 (2004).
[14] B. Wei, S. Xie, M. Yu, et al., QSPR-based prediction of gas/particle partitioning of polychlorinated biphenyls in the atmosphere, Chemosphere, 66(10), 1807-1820 (2007).
[15] J. F. Niu, Z. F. Yang, Z. Y. Shen, et al., QSPRs for the predic- tion of photodegradation half-life of PCBs in n-hexane, SAR QSAR Environ. Res., 17(2), 173-182 (2006).
[16] J. Padmanabhan, R. Parthasarathi, V. Subramanian, et al., QSPR models for polychlorinated biphenyls: n- Octanol/water partition coefficient, Bioorg. Med. Chem. Lett., 14(4), 1021-1028 (2006).
[17] L.Jantschi,S.Bolboaca ̆,MolecularDescriptorsFamilyon Structure Activity Relationships 6. Octanol-Water Partition Coefficient of Polychlorinated Biphenyls, Leonardo El. J. Pract. Technol. 8, 71-86 (2006).
[18] S. Puri, J. S. Chickos and W.J. Welsh, Three-dimensional quantitative structure – Property relationship (3D-QSPR) models for prediction of thermodynamic properties of poly- chlorinated biphenyls (PCBs): Enthalpy of vaporization, J. Chem. Inf. Comp. Sci. 42(2), 299-304 (2002).
[19] J. Padmanabhan, R. Parthasarathi, V. Subramanian, et al.,
Using QSPR models to predict the enthalpy of vaporization of 209 polychlorinated biphenyl congeners, QSAR Comb. Sci. 26(2), 227-237 (2007).
[20] S. Puri, J.S. Chickos and W.J. Welsh, Three-dimensional quantitative structure – Property relationship (3D-QSPR) models for prediction of thermodynamic properties of poly- chlorinated biphenyls (PCBs): Enthalpy of sublimation, J. Chem. Inf. Comp. Sci. 42(1), 109-116 (2002).
[21] J.DevillersandZ.Fresenius,Asimplemethodforthepredic- tion of the GLC retention times of all the 209 PCB congeners. Anal. Chem. 332 (1), 61-62 (1988).
[22] M.N. Hasan and P.C. Jurs, Computer-assisted prediction of gas chromatographic retention times of polychlorinated biphenyls, Anal. Chem., 60(10), 978-982 (1988).
[23] S.Liu,Y.Liu,D.Yin,etal.,Predictionofchromatographic relative retention time of polychlorinated biphenyls from the molecular electronegativity distance vector, J. Sep. Sci. 29(2), 296-301(2006).
[24] Y.Ren,H.Liu,X.Yao,etal.,AnaccurateQSRRmodelfor the prediction of the GC×GCTOFMS retention time of poly- chlorinated biphenyl (PCB) congeners, Anal. Bioanal. Chem. 388 (1), 165-172 (2007).
[25] S.Bowadt,H.Skejoandresen,L.Montanarella,etal.,Hrgc separations of 160 chlorobiphenyls in technical mixtures on 4 polar narrow-bore columns, Int. J. Environ. Anal. Chem. 56(2), 87-107(1994).
[26] V. Gajduskova and R. Uldrich, Analysis of specific poly- chlorinated biphenyl congeners for the examination of raw- materials and foodstuffs of animal origin, Vet. Med. 37, 471- 478 (1992).
[27] M.Bolgar,J.Cunningham,R.Cooper,etal.„Physical,spec- tral and chromatographic properties of all 209 individual PCB congeners, Chemosphere 31, 2687-2705 (1995).
[28] G. Castello and G. Testini, Determination of retention in- dices of polychlorobiphenyls by using other compounds de- tectable by electron-capture detection or selected polychloro- biphenyls as the reference series, J. Chromatogr. A 741, 241- 249 (1996).
[29] M. D. Mullin, C.M. Pochini, S. McCrindle, et al., High- resolution PCB analysis: synthesis and chromatographic properties of all 209 PCB congeners, Environ. Sci. Tech- nol., 18,468-476 (1994).
[30] A.RobbatJr.,G.XyrafasandD.Marshall,Predictionofgas chromatographic retention characteristic of polychlorinated biphenyls, Anal. Chem. 60, 982-985 (1988).
[31] H.A.J.GoversandP.deVoogt,Gaschromatographicderiva- tion of the solubility parameters of polychbrinated biphenyls with the inclusion of cis-trans and optical isomerism and orientational disorder, SAR QSAR Environ. Res. 3, 315-324 (1995).
[32] J. Ghasemi and S. Saaidpour, QSPR prediction of aqueous solubility of drug-like organic compounds, Chem. Pharm. Bull. 55, 669-674 (2007).
[33] J. Ghasemi, S. Saaidpour and S.D. Brown, QSPR study for estimation of acidity constants of some aromatic acids deriva- tives using multiple linear regression (MLR) analysis, J. Mol. Struct. (Theochem.) 805, 27-32 (2007).
[34] J.GhasemiandS.Saaidpour,Quantitativestructure-property relationship study of n-octanol- water partition coefficients of some of diverse drugs using multiple linear regression, Anal. Chim. Acta 604, 99-106 (2007).
[35] J. Ghasemi and S. Saaidpour, QSRR prediction of the chro- matographic retention behavior of painkiller drugs, J. Chro- matogr. Sci. 47, 156-163 (2009).
[36] J.GhasemiandS.Saaidpour,Artificialneuralnetwork-based quantitative structural property relationship for predicting boiling points of refrigerants, QSAR Comb. Sci., 28, 1245- 1254 (2009).
[37] S. Saaidpour, Prediction of drug lipophilicity using back prop- agation artificial neural network modeling, Orient. J. Chem. 30(2), 793-802(2014).
[38] S. Saaidpour, A. Bahmani and A. Rostami, Prediction the normal boiling points of primary, secondary and tertiary liq- uid amines from their molecular structure descriptors, CMST 21(4) 201-210 (2015).
[39] S. Khaledian and S. Saaidpour, Quantitative structure- property relationship modelling of distribution coefficients (logd7.4) of diverse drug by sub-structural molecular frag- ments method, Orient. J. Chem. 31(4), 1969-1976(2015).
[40] S. Saaidpour, Quantitative modeling for prediction of criti- cal temperature of refrigerant compounds, Phys. Chem. Res. 4(1), 61-71(2016).
[41] S. Saaidpour, S. A. Zarei and F. Nasri, QSPR study of molar diamagnetic susceptibility of diverse organic compounds us- ing multiple linear regression analysis, Pak. J. Chem. 2(1),1- 12(2012).
[42] P. Gramatica, N. Navas and R. Todeschini, 3D-Modelling and Prediction by WHIM Descriptors. Part 9. Chromatographic Relative Retention Time and Physico-Chemical Properties of Polychlorinated Biphenyls (PCBs), Chemom. Intell. Lab. Syst. 40, 53-63(1998).
[43] V.P.Solovev,A.VarnekandG.Wipff,ModelingofIonCom- plexation and Extraction Using Substructural Molecular Fragments, J. Chem. Inf. Comput. Sci. 40, 847-858 (2000).
[44] A.Varnek,G.WipffandV.P.Solovev,TowardsanInforma- tion System on Solvent Extraction, Solvent Extr. Ion Exc. 19,791-837 (2001).
[45] A.Varnek,G.Wipff,V.P.Solovev,etal.,Assessmentofthe macrocyclic effect for the complexation of crown-ethers with alkali cations using the substructural molecular fragments method, J. Chem. Inf. Comput. Sci. 42(4), 812-829 (2002).
[46] V. P. Solovev and A. Varnek, Anti-HIV activity of hept, tibo and cyclic urea derivatives: structure-property studies, fo- cused combinatorial library generation and hits selection using substructural molecular fragments method, J. Chem. Inf. Comp. Sci. 43(5), 1703-1719(2003).
[47] V.P.SolovevandA.Varnek,Structure-propertymodelingof metal binders using molecular fragments, Russ. Chem. Bull. 53,1434-1445(2004).
[48] A.VarnekandV.P.Solovev,“InSilico”designofpotential anti-hiv actives using fragment descriptors, Comb. Chem. High T. Scr. 8(5), 403-416 (2005).
[49] A.Varnek, D. Fourches, F. Hoonakker, et al., Substructural fragments: an universal language to encode reactions, molec- ular and supramolecular structures, J. Comput. Aided Mol. Des. 19, 693-703 (2005).
[50] A. Dalby, J. G. Nourse, W. D. Hounshell, et al., Description of several chemical structure file formats used by computer programs developed at molecular design limited, J. Chem. Inf. Comput. Sci. 32, 244-255 (1992).
Quantitative structure-retention relationship (QSRR) analysis is a useful technique capable of relating chromato- graphic retention time to the chemical structure of a solute. Using the sub-structural molecular fragments (SMF) derived directly from the molecular structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were calculated. An eight-variable regression equation with the correlation coefficient of 0.9945 and the root mean square errors of 0.0134 was developed. Forward and backward stepwise regression variable selection and multi-linear regression analysis (MLRA) are combined to describe the effect of molecular structure on the RRT of PCB according to the QSRR method. To quantitatively relate RRT with the molecular structure MLR analysis is performed on the set of 163 sub-structural molecular fragments (SMF) provided by the ISIDA software. The eight fragments selected by variable subset selection, all belonging to the sub-fragments, adequately represent the structural factors influencing the affinity of PCB to SE-54 stationary phase in the separation process. Finally, a QSRR model is selected based on leave-one-out cross-validation and its prediction ability is further tested on 42 representative compounds excluded from model calibration. The prediction results from the MLR model are in good agreement with the experimental values. By applying the MLR method we can predict the test set with squared cross validated correlation coefficient (Q2ext) of 0.9913 and root mean square error (RMSE) of 0.0169.
Key words:
MLR, polychlorinated biphenyl, QSRR, relative retention time, sub-structural molecular fragment
References:
[1] C.J. Halsall, R.G.M. Lee, P.J. Coleman, et al., PCBs in UK urban air, Environ. Sci. Technol., 29, 2368-2376 (1995).
[2] R.F.Herrick,D.J.LefkowitzandG.A.Weymouth,Soilcon- tamination from PCB containing buildings, Environ. Health Persp., 115, 173-175 (2007).
[3] S. Bayen, E. Koroleva, H.K. Lee, et al., Persistent organic pollutants and heavy metals in typical sea foods consumed in Singapore, J. Toxicol. Environ. Health, Part A 68, 151-166 (2005).
[4] M.P.Simmonds,K.Haraguchi,T.Endo,etal.,Humanhealth significance of organochlorine and mercury contaminants in Japanese whale meat, J. Toxicol. Environ. Health, Part A, 65, 1211-1235(2002).
[5] D.G. Wang, M. Yang, H.L. Jia, et al., Levels, distributions and profiles of polychlorinated biphenyls in surface soils of Dalian, China, Chemosphere, 73, 38-42 (2008).
[6] S. Ohta, H. Tokusawa, T. Nakao, et al., Global contamina- tion of coplanar polybrominated/chlorinated biphenyls (Co- PXBs) in the market fishes from Japan, Chemosphere, 73, 31-38 (2008).
[7] J. She, A. Holden, T.L. Adelsbach, et al., Concentrations and time trends of polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) in aquatic bird eggs from San Francisco Bay, CA 2000-2003, Chemosphere, 73, 201-209 (2008).
[8] A.S. Souza, J.P.M. Torres, R.O. Meire, et al., Organochlo- rine pesticides (OCs) and polychlorinated biphenyls (PCBs) in sediments and crabs (Chasmagnathus granulata, Dana, 1851) from mangroves of Guanabara bay, Rio de Janeiro state, Brazil, Chemosphere, 73, 186-192 (2008).
[9] C.Mori,H.Fukata,K.Sakurai,etal.,Strongcorrelationbe- tween the concentration of dioxins and total PCBs in current Japanese people, Chemosphere, 73, 235-238 (2008).
[10] Y. Masuda, A. Schecter, and O. Papke, Concentrations of PCBs, PCDFs and PCDDs in the blood of Yusho patients and their toxic equivalent contribution, Chemosphere, 37, 1773-1780 (1998).
[11] S.L. Schantz, Developmental neurotoxicity of PCBs in hu- mans: what do we know and where do we go from here?, Neurotoxicol Teratol 18, 217-227 (1996).
[12] M.D.Erickson,AnalyticalchemistryofPCBs,2ndedn.CRC Press LLC, Boca Raton, FL, USA 1997.
[13] E.A. Castro, A. A. Toropov, A.I. Nesterova ,et al., QSPR modeling aqueous solubility of polychlorinated biphenyls by optimization of correlation weights of local and global graph invariants, Central European Journal of Chemistry, 2(3), 500-523 (2004).
[14] B. Wei, S. Xie, M. Yu, et al., QSPR-based prediction of gas/particle partitioning of polychlorinated biphenyls in the atmosphere, Chemosphere, 66(10), 1807-1820 (2007).
[15] J. F. Niu, Z. F. Yang, Z. Y. Shen, et al., QSPRs for the predic- tion of photodegradation half-life of PCBs in n-hexane, SAR QSAR Environ. Res., 17(2), 173-182 (2006).
[16] J. Padmanabhan, R. Parthasarathi, V. Subramanian, et al., QSPR models for polychlorinated biphenyls: n- Octanol/water partition coefficient, Bioorg. Med. Chem. Lett., 14(4), 1021-1028 (2006).
[17] L.Jantschi,S.Bolboaca ̆,MolecularDescriptorsFamilyon Structure Activity Relationships 6. Octanol-Water Partition Coefficient of Polychlorinated Biphenyls, Leonardo El. J. Pract. Technol. 8, 71-86 (2006).
[18] S. Puri, J. S. Chickos and W.J. Welsh, Three-dimensional quantitative structure – Property relationship (3D-QSPR) models for prediction of thermodynamic properties of poly- chlorinated biphenyls (PCBs): Enthalpy of vaporization, J. Chem. Inf. Comp. Sci. 42(2), 299-304 (2002).
[19] J. Padmanabhan, R. Parthasarathi, V. Subramanian, et al.,
Using QSPR models to predict the enthalpy of vaporization of 209 polychlorinated biphenyl congeners, QSAR Comb. Sci. 26(2), 227-237 (2007).
[20] S. Puri, J.S. Chickos and W.J. Welsh, Three-dimensional quantitative structure – Property relationship (3D-QSPR) models for prediction of thermodynamic properties of poly- chlorinated biphenyls (PCBs): Enthalpy of sublimation, J. Chem. Inf. Comp. Sci. 42(1), 109-116 (2002).
[21] J.DevillersandZ.Fresenius,Asimplemethodforthepredic- tion of the GLC retention times of all the 209 PCB congeners. Anal. Chem. 332 (1), 61-62 (1988).
[22] M.N. Hasan and P.C. Jurs, Computer-assisted prediction of gas chromatographic retention times of polychlorinated biphenyls, Anal. Chem., 60(10), 978-982 (1988).
[23] S.Liu,Y.Liu,D.Yin,etal.,Predictionofchromatographic relative retention time of polychlorinated biphenyls from the molecular electronegativity distance vector, J. Sep. Sci. 29(2), 296-301(2006).
[24] Y.Ren,H.Liu,X.Yao,etal.,AnaccurateQSRRmodelfor the prediction of the GC×GCTOFMS retention time of poly- chlorinated biphenyl (PCB) congeners, Anal. Bioanal. Chem. 388 (1), 165-172 (2007).
[25] S.Bowadt,H.Skejoandresen,L.Montanarella,etal.,Hrgc separations of 160 chlorobiphenyls in technical mixtures on 4 polar narrow-bore columns, Int. J. Environ. Anal. Chem. 56(2), 87-107(1994).
[26] V. Gajduskova and R. Uldrich, Analysis of specific poly- chlorinated biphenyl congeners for the examination of raw- materials and foodstuffs of animal origin, Vet. Med. 37, 471- 478 (1992).
[27] M.Bolgar,J.Cunningham,R.Cooper,etal.„Physical,spec- tral and chromatographic properties of all 209 individual PCB congeners, Chemosphere 31, 2687-2705 (1995).
[28] G. Castello and G. Testini, Determination of retention in- dices of polychlorobiphenyls by using other compounds de- tectable by electron-capture detection or selected polychloro- biphenyls as the reference series, J. Chromatogr. A 741, 241- 249 (1996).
[29] M. D. Mullin, C.M. Pochini, S. McCrindle, et al., High- resolution PCB analysis: synthesis and chromatographic properties of all 209 PCB congeners, Environ. Sci. Tech- nol., 18,468-476 (1994).
[30] A.RobbatJr.,G.XyrafasandD.Marshall,Predictionofgas chromatographic retention characteristic of polychlorinated biphenyls, Anal. Chem. 60, 982-985 (1988).
[31] H.A.J.GoversandP.deVoogt,Gaschromatographicderiva- tion of the solubility parameters of polychbrinated biphenyls with the inclusion of cis-trans and optical isomerism and orientational disorder, SAR QSAR Environ. Res. 3, 315-324 (1995).
[32] J. Ghasemi and S. Saaidpour, QSPR prediction of aqueous solubility of drug-like organic compounds, Chem. Pharm. Bull. 55, 669-674 (2007).
[33] J. Ghasemi, S. Saaidpour and S.D. Brown, QSPR study for estimation of acidity constants of some aromatic acids deriva- tives using multiple linear regression (MLR) analysis, J. Mol. Struct. (Theochem.) 805, 27-32 (2007).
[34] J.GhasemiandS.Saaidpour,Quantitativestructure-property relationship study of n-octanol- water partition coefficients of some of diverse drugs using multiple linear regression, Anal. Chim. Acta 604, 99-106 (2007).
[35] J. Ghasemi and S. Saaidpour, QSRR prediction of the chro- matographic retention behavior of painkiller drugs, J. Chro- matogr. Sci. 47, 156-163 (2009).
[36] J.GhasemiandS.Saaidpour,Artificialneuralnetwork-based quantitative structural property relationship for predicting boiling points of refrigerants, QSAR Comb. Sci., 28, 1245- 1254 (2009).
[37] S. Saaidpour, Prediction of drug lipophilicity using back prop- agation artificial neural network modeling, Orient. J. Chem. 30(2), 793-802(2014).
[38] S. Saaidpour, A. Bahmani and A. Rostami, Prediction the normal boiling points of primary, secondary and tertiary liq- uid amines from their molecular structure descriptors, CMST 21(4) 201-210 (2015).
[39] S. Khaledian and S. Saaidpour, Quantitative structure- property relationship modelling of distribution coefficients (logd7.4) of diverse drug by sub-structural molecular frag- ments method, Orient. J. Chem. 31(4), 1969-1976(2015).
[40] S. Saaidpour, Quantitative modeling for prediction of criti- cal temperature of refrigerant compounds, Phys. Chem. Res. 4(1), 61-71(2016).
[41] S. Saaidpour, S. A. Zarei and F. Nasri, QSPR study of molar diamagnetic susceptibility of diverse organic compounds us- ing multiple linear regression analysis, Pak. J. Chem. 2(1),1- 12(2012).
[42] P. Gramatica, N. Navas and R. Todeschini, 3D-Modelling and Prediction by WHIM Descriptors. Part 9. Chromatographic Relative Retention Time and Physico-Chemical Properties of Polychlorinated Biphenyls (PCBs), Chemom. Intell. Lab. Syst. 40, 53-63(1998).
[43] V.P.Solovev,A.VarnekandG.Wipff,ModelingofIonCom- plexation and Extraction Using Substructural Molecular Fragments, J. Chem. Inf. Comput. Sci. 40, 847-858 (2000).
[44] A.Varnek,G.WipffandV.P.Solovev,TowardsanInforma- tion System on Solvent Extraction, Solvent Extr. Ion Exc. 19,791-837 (2001).
[45] A.Varnek,G.Wipff,V.P.Solovev,etal.,Assessmentofthe macrocyclic effect for the complexation of crown-ethers with alkali cations using the substructural molecular fragments method, J. Chem. Inf. Comput. Sci. 42(4), 812-829 (2002).
[46] V. P. Solovev and A. Varnek, Anti-HIV activity of hept, tibo and cyclic urea derivatives: structure-property studies, fo- cused combinatorial library generation and hits selection using substructural molecular fragments method, J. Chem. Inf. Comp. Sci. 43(5), 1703-1719(2003).
[47] V.P.SolovevandA.Varnek,Structure-propertymodelingof metal binders using molecular fragments, Russ. Chem. Bull. 53,1434-1445(2004).
[48] A.VarnekandV.P.Solovev,“InSilico”designofpotential anti-hiv actives using fragment descriptors, Comb. Chem. High T. Scr. 8(5), 403-416 (2005).
[49] A.Varnek, D. Fourches, F. Hoonakker, et al., Substructural fragments: an universal language to encode reactions, molec- ular and supramolecular structures, J. Comput. Aided Mol. Des. 19, 693-703 (2005).
[50] A. Dalby, J. G. Nourse, W. D. Hounshell, et al., Description of several chemical structure file formats used by computer programs developed at molecular design limited, J. Chem. Inf. Comput. Sci. 32, 244-255 (1992).
