DFT-based reactivity and QSPR studies of platinum (IV) anticancer drugs.

In the present work the influence of different axial ligands on reactivity of some selected Pt(IV) complexes with anticancer activities are investigated in both gas and solvent phases using Density Functional Theory (DFT) calculations. Calculated geometries of the complexes are in good agreement with their available X-ray data. The reactivity descriptors such as hardness, chemical potential and electrophilicity are calculated to measure stability and reactive nature of the complexes. It has been interesting to observe that the increase in the number of carbon chain of carboxylato axial ligand has no influence on reactivity of Pt(IV) complexes. Multiple linear regression analyses are performed to build Quantitative Structure Property Relationship (QSPR) models using DFT and molecular mechanics (MM+) based descriptors in gas and solvent phases. Chemical potential is found to be the most significant single descriptor to measure reduction potential of Pt(IV) complexes giving 87% correlation with experimental data. While gas phase derived descriptors are not statistically significant, inclusion of solvent medium increases the correlation of each descriptor with reduction potential and hydrophobicity of the complexes.

Quantitative structure-activity relationships of the antimalarial agent artemisinin and some of its derivatives - a DFT approach.

Artemisinin form the most important class of antimalarial agents currently available, and is a unique sesquiterpene peroxide occurring as a constituent of Artemisia annua. Artemisinin is effectively used in the treatment of drug-resistant Plasmodium falciparum and because of its rapid clearance of cerebral malaria, many clinically useful semisynthetic drugs for severe and complicated malaria have been developed. However, one of the major disadvantages of using artemisinins is their poor solubility either in oil or water and therefore, in order to overcome this difficulty many derivatives of artemisinin were prepared. A comparative study on the chemical reactivity of artemisinin and some of its derivatives is performed using density functional theory (DFT) calculations. DFT based global and local reactivity descriptors, such as hardness, chemical potential, electrophilicity index, Fukui function, and local philicity calculated at the optimized geometries are used to investigate the usefulness of these descriptors for understanding the reactive nature and reactive sites of the molecules. Multiple regression analysis is applied to build up a quantitative structure-activity relationship (QSAR) model based on the DFT based descriptors against the chloroquine-resistant, mefloquine-sensitive Plasmodium falciparum W-2 clone.

DFT-based QSAR models to predict the antimycobacterial activity of chalcones.

In this study, antimycobacterial activity of a set of synthesized chalcone derivatives against Mycobacterium tuberculosis H37Rv was investigated by quantitative structure-activity relationship (QSAR) analysis using density functional theory (DFT) and molecular mechanics (MM+)-based descriptors in both gas and solvent phases. The best molecular descriptors identified were hardness, E(HOMO) , MR(A-4) and MR(B-4') that contributed to the antimycobacterial activity of the chalcones as independent factors. The correlation of these four descriptors with their antimycobacterial activity increases with the inclusion of solvent medium, indicating their importance in studying biological activity. QSAR models revealed that in gas phase, lower values of E(HOMO) , MR(A-4) and MR(B-4') increase the antimycobacterial activity of the chalcone molecules. However, in solvent phase, lower values of E(HOMO) and MR(B-4') and higher values of MR(A-4) increase their activity.

Anticancer activity of nucleoside analogues: a density functional theory based QSAR study.

In the present work multiple linear regression analyses were performed to build QSAR models for nucleoside analogous using density functional theory (DFT) and molecular mechanics (MM+) based descriptors in both gas and solvent phases. The QSAR models for 14 carbocyclic analogues of nucleosides against murine leukemia cell line (L1210/0) and human T-lymphocyte cell lines (Molt4/C8 and CEM/0) explain more than 90% of the variances in the activity data along with higher values of [Formula: see text]. The energy of the next lowest unoccupied molecular orbital (E(NL)), electrophilicity (omega) and van der Waals surface area (SA) are the main independent factors contributing to the anticancer activity of nucleoside analogues. Inclusion of solvent medium increases the correlation of each descriptor with activity. Based on the key features responsible for anticancer activity, 10 new compounds with rather high anticancer activity have been theoretically designed. Cytotoxic activities of an additional set of 20 nucleoside analogues were also modeled by the same descriptors and found their predicted values to be in good agreement with the experimental values.

DFT-based QSAR and QSPR models of several cis-platinum complexes: solvent effect.

Cytotoxic activities of cis-platinum complexes against parental and resistant ovarian cancer cell lines were investigated by quantitative structure-activity relationship (QSAR) analysis using density functional theory (DFT) based descriptors. The calculated parameters were found to increase the predictability of each QSAR model with incorporation of solvent effects indicating its importance in studying biological activity. Given the importance of logarithmic n-octanol/water partition coefficient (log P(o/w)) in drug metabolism and cellular uptake, we modeled the log P(o/w) of 24 platinum complexes with different leaving and carrier ligands by the quantitative structure-property relationship (QSPR) analysis against five different concentrations of MeOH using DFT and molecular mechanics derived descriptors. The log P(o/w) values of an additional set of 20 platinum complexes were also modeled with the same descriptors. We investigated the predictability of the model by calculating log P(o/w) of four compounds in the test set and found their predicted values to be in good agreement with the experimental values. The QSPR analyses performed on 24 complexes, combining the training and test sets, also provided significant values for the statistical parameters. The solvent medium played an important role in QSPR analysis by increasing the internal predictive ability of the models.