Abstract
Problem statement: QSAR techniques increase the probability of success and reduce time and coast in drug discovery process. The study presented QSAR investigation on 32 bioactive aziridinylbenzoquinones that have activity against lymphoid leukemia. Approach: Molecular descriptors, molecular weight, total energy, hardness, chemical potential, electrophilicity index, HOMO and LUMO energies were calculated. Initial geometry optimizations were carried out with the AM1 Hamiltonian. The lowest energy conformations were subjected to single point calculations by the DFT method by employing Beck's Three-Parameter hybrid functional (B3LYP) and pvDZ basis set. Several models for the prediction of biological activity have been drawn up by using the multiple regression technique. Results: A model with hapta parametric linear equation with R2 value of 0.886 was presented. Conclusion: The biological activity of the studied compounds can be modeled with quantum chemical molecular descriptors.
Highlights
Quantum chemical descriptors have been extensively used in Quantitave Structure-Activity Relationship studies in biochemistry
For the calculation of the quantum chemical molecular descriptor used in QSAR studies, semi empirical methods such as AM1 and PM3 mainly have been used (Cavalli et al, 2006; Shaik et al, 2005)
Less significant than Molecular weight (Mw) and TE is ω, which when not involved in the model equation the R2 value dropped to 0.851 Eq 10: Log (1 / MED) = 97.131 – 0.008 Mw – 0.0022 TE
Summary
Quantum chemical descriptors have been extensively used in Quantitave Structure-Activity Relationship studies in biochemistry. For the calculation of the quantum chemical molecular descriptor used in QSAR studies, semi empirical methods such as AM1 and PM3 mainly have been used (Cavalli et al, 2006; Shaik et al, 2005). Several aziridinylquinones have undergone clinical trials as potential antitumor drugs (Rajski and Williams, 1997; Mayalarp et al, 1996; Moret et al, 1996; Gupta, 1994) These compounds can be activated toward alkylation as a result of bioreduction by the electron reducing enzymes or by two electron reducing compounds (Aiello et al, 2005). The aim of this study is to build QSAR models using multiple regression method, to investigate the correlations between the experimental biological activity and calculated molecular descriptors of a series of 2,5Bis(1-aziridinyl)-p-benzoquinones as inhibitors against lymphoid leukemia L1210 in BDF1 mice
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