Year 2022,
, 1 - 8, 15.06.2022
Rabah Khalil
,
Shayma'a H. Abdulrahman
References
- [1] Y. Ran, S.H. Yalkowsky, Prediction of drug solubility by the general solubility equation (GSE), J Chem Inf Comput Sci 41 (2001) 354–357.
- [2] W.L. Jorgensen, E. M. Duffy, Prediction of drug solubility from structure. Adv Drug Deliv Rev 45 (2002) 355-366.
- [3] R.A. Khalil, A.Y. Hamed, Theoretical investigation using DFT for predicting the factors affecting the melting point of series of alkylammoniumformates ionic liquids, Arab J Phys Chem 2 (2015) 56-63.
- [4] I.V. Tetko, Y. Sushko, S. Novotarskyi, L. Patiny, I. Kondratov, A. E. Petrenko, L. Charochkina, A. M. Asiri, How accurately can we predict the melting points of drug-like compounds? J Chem Info Model 54 (2014) 3320-3329.
- [5] I.V. Tetko, D. M. Lowe, A.J. Williams, The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS, J Cheminform 8 (2016) 186-203.
- [6] C.A.S. Bergström, U. Norinder, K. Luthman, P. Artursson, Molecular descriptors influencing melting point and their role in classification of solid drugs, J Chem Inf Comput. Sci 43 (2003) 1177-1185.
- [7] H. Modarresi, J.C. Dearden, H. Modarress, QSPR correlation of melting point for drug compounds based on different sources of molecular descriptors, J Chem Info Model 46 (2006) 930-936.
- [8] A. R. Katritzky, U. Maran, M. Karelson, V.S. Lobanov, Prediction of melting points for the substituted benzenes: A QSPR approach. J Chem Inf Comput. Sci 37 (1997) 913-919.
- [9] B. Johnson-Restrepo, L. Pacheco-Londoño, J. Olivero-Verbel, Molecular Parameters Responsible for the Melting Point of 1,2,3-Diazaborine Compounds, J Chem Inf Comput. Sci 43 (2003) 1513-1519.
- [10] R.F. Alamdari, M.H. Keshavarz, A simple method to predict melting points of non-aromatic energetic compounds. Fluid Phase Equilibria 292 (2010) 1–6.
- [11] A. Guendouzi, S.M. Mekelleche, Prediction of the melting points of fatty acids from computed molecular descriptors: A quantitative structure–property relationship study, Chem Phys Lipids 165 (2012) 1-6.
- [12] J.A. Morrill, E.F.C. Byrd, Development of quantitative structure property relationships for predicting the melting point of energetic materials, J Mol Graph Model 62 (2015) 190–201.
- [13] D. Wang, Y. Yuan, S. Duan, R. Liu, S. Gu, S. Zhao, L, Liu, J. Xu, QSPR study on melting point of carbocyclic nitroaromatic compounds by multiple linear regression and artificial neural network, Chemom Intell Lab Syst 143 (2015) 7-15.
- [14] M. Kumar, K. Ramasamy, V. Mani, R. K. Mishra, A. Abdul Majeed, E. De Clercq, B. Narasimhan. Synthesis, antimicrobial, anticancer, antiviral evaluation and QSAR studies of 4-(1-aryl-2-oxo-1,2-dihydro-indol-3-ylideneamino)-N-substituted benzene sulfonamides, Arabian J Chem 7 (2014) 396-408.
- [15] R.A. Khalil, A.A. Zarari, Theoretical investigations for the behavior of hydrotropes in aqueous solution, J Turk Chem Soc A 2 (2015) 42-52.
- [16] R.A. Khalil, A.A. Zarari, Theoretical estimation of the critical packing parameter of amphiphilic self-assembled aggregates, Appl Surf Sci 318 (2014) 85-59.
- [17] M. Črepnjak, N. Tratnik, P.Ž. Pleteršek, Predicting melting points of hydrocarbons by the Graovac-Pisanski index, Fullerenes, Nanotubes Carbon Nanostructures 26 (2018) 1080-1101.
- [18] B. Tüzün, E. Saripinar, Molecular docking and 4D-QSAR model of methanone derivatives by electron conformational-genetic algorithm method, J Iran Chem Soc 17(2020) 985-1000.
- [19] B.Tuzun, S.C. Yavuz, N.Sabanci, E. Saripinar, 4D-QSAR Study of some pyrazole pyridine carboxylic acid derivatives by electron conformational-genetic algorithm method, Current Computer-Aided Drug Design 14 (2018) 370- 384.
- [20] B. Tuzun, S.C. Yavuz, E. Saripinar, 4D-QSAr analysis and pharmacophore modeling: propoxy methylphenyl oxasiazole derivatives by electron conformatitional-genetic algorithm method, J. Physical Theoretical chem 14 (2017) 149-164.
A Developed QSPR Model for the Melting Points of Isatin Derivatives
Year 2022,
, 1 - 8, 15.06.2022
Rabah Khalil
,
Shayma'a H. Abdulrahman
Abstract
This paper suggests a developed quantitative structure property relationship (QSPR) model for coping the melting point (M.P) which is considered as the main and important physical property of solid state. The development was based on the decreasing in number of descriptors in order to be statistically intensive with excellent values of statistical parameters. The model was applied successfully to the already published data of M.P for 32 biologically active molecules derived from 4-(1-aryl-2-oxo-1,2-dihydro-indol-3-ylideneamino)-N-substituted benzene sulfonamides. The calculations of descriptors were carried out using density functional theory (DFT) with bases set of 6-311G (d, P). A statistically intensive QSPR model contains only three descriptors with physical meaning has been introduced. Two of them are belonging to the direct theoretical calculations but the third was considered as three dimensional correcting term of which depending on the chemical structure of the substituent. The theoretically calculated descriptors were the total connectivity (TC) and the average charge on the aryl group (AQArH) as both depending on the packing of molecules and responsible on M.P. The last descriptor was suggested as a correction term with respect to the packing of molecules of which depending on their three dimensional chemical structure which only taking the values of -1, 0 and 1. A relatively excellent statistical parameters for the developed model were obtained with square regression coefficient (r2), cross-validation (q2) and root mean squared error (RMSE) are equal to 0.925, 0.903 and 15.26oC, respectively. It was concluded that the developed model gives more confidence results in addition to physical significance which can be considered as a helpful tool for understanding the factors affecting the melting point.
References
- [1] Y. Ran, S.H. Yalkowsky, Prediction of drug solubility by the general solubility equation (GSE), J Chem Inf Comput Sci 41 (2001) 354–357.
- [2] W.L. Jorgensen, E. M. Duffy, Prediction of drug solubility from structure. Adv Drug Deliv Rev 45 (2002) 355-366.
- [3] R.A. Khalil, A.Y. Hamed, Theoretical investigation using DFT for predicting the factors affecting the melting point of series of alkylammoniumformates ionic liquids, Arab J Phys Chem 2 (2015) 56-63.
- [4] I.V. Tetko, Y. Sushko, S. Novotarskyi, L. Patiny, I. Kondratov, A. E. Petrenko, L. Charochkina, A. M. Asiri, How accurately can we predict the melting points of drug-like compounds? J Chem Info Model 54 (2014) 3320-3329.
- [5] I.V. Tetko, D. M. Lowe, A.J. Williams, The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS, J Cheminform 8 (2016) 186-203.
- [6] C.A.S. Bergström, U. Norinder, K. Luthman, P. Artursson, Molecular descriptors influencing melting point and their role in classification of solid drugs, J Chem Inf Comput. Sci 43 (2003) 1177-1185.
- [7] H. Modarresi, J.C. Dearden, H. Modarress, QSPR correlation of melting point for drug compounds based on different sources of molecular descriptors, J Chem Info Model 46 (2006) 930-936.
- [8] A. R. Katritzky, U. Maran, M. Karelson, V.S. Lobanov, Prediction of melting points for the substituted benzenes: A QSPR approach. J Chem Inf Comput. Sci 37 (1997) 913-919.
- [9] B. Johnson-Restrepo, L. Pacheco-Londoño, J. Olivero-Verbel, Molecular Parameters Responsible for the Melting Point of 1,2,3-Diazaborine Compounds, J Chem Inf Comput. Sci 43 (2003) 1513-1519.
- [10] R.F. Alamdari, M.H. Keshavarz, A simple method to predict melting points of non-aromatic energetic compounds. Fluid Phase Equilibria 292 (2010) 1–6.
- [11] A. Guendouzi, S.M. Mekelleche, Prediction of the melting points of fatty acids from computed molecular descriptors: A quantitative structure–property relationship study, Chem Phys Lipids 165 (2012) 1-6.
- [12] J.A. Morrill, E.F.C. Byrd, Development of quantitative structure property relationships for predicting the melting point of energetic materials, J Mol Graph Model 62 (2015) 190–201.
- [13] D. Wang, Y. Yuan, S. Duan, R. Liu, S. Gu, S. Zhao, L, Liu, J. Xu, QSPR study on melting point of carbocyclic nitroaromatic compounds by multiple linear regression and artificial neural network, Chemom Intell Lab Syst 143 (2015) 7-15.
- [14] M. Kumar, K. Ramasamy, V. Mani, R. K. Mishra, A. Abdul Majeed, E. De Clercq, B. Narasimhan. Synthesis, antimicrobial, anticancer, antiviral evaluation and QSAR studies of 4-(1-aryl-2-oxo-1,2-dihydro-indol-3-ylideneamino)-N-substituted benzene sulfonamides, Arabian J Chem 7 (2014) 396-408.
- [15] R.A. Khalil, A.A. Zarari, Theoretical investigations for the behavior of hydrotropes in aqueous solution, J Turk Chem Soc A 2 (2015) 42-52.
- [16] R.A. Khalil, A.A. Zarari, Theoretical estimation of the critical packing parameter of amphiphilic self-assembled aggregates, Appl Surf Sci 318 (2014) 85-59.
- [17] M. Črepnjak, N. Tratnik, P.Ž. Pleteršek, Predicting melting points of hydrocarbons by the Graovac-Pisanski index, Fullerenes, Nanotubes Carbon Nanostructures 26 (2018) 1080-1101.
- [18] B. Tüzün, E. Saripinar, Molecular docking and 4D-QSAR model of methanone derivatives by electron conformational-genetic algorithm method, J Iran Chem Soc 17(2020) 985-1000.
- [19] B.Tuzun, S.C. Yavuz, N.Sabanci, E. Saripinar, 4D-QSAR Study of some pyrazole pyridine carboxylic acid derivatives by electron conformational-genetic algorithm method, Current Computer-Aided Drug Design 14 (2018) 370- 384.
- [20] B. Tuzun, S.C. Yavuz, E. Saripinar, 4D-QSAr analysis and pharmacophore modeling: propoxy methylphenyl oxasiazole derivatives by electron conformatitional-genetic algorithm method, J. Physical Theoretical chem 14 (2017) 149-164.