To see the future, we must look at the past (German proverb).A QSAR (quantitative structure-activity relationship) is a mathematical equation that relates a property of interest such as a biological potency with one or more molecular properties (called descriptors), for a series of (usually) related compounds...

The implications for the ability of energy descriptors heat of formation, total energy, highest occupied molecular orbital energy, lowest unoccupied molecular orbital energy, electron affinity and ionization potential to describe the biological activities of peptidic HIV-1 protease inhibitors are discussed.The relationships between observed biological activity, log1/C, and the descriptors are established.The suitability of these parameters has been tested by predicting the biological activity of inhibitors.For prediction of the activity of the inhibitors, QSAR model have been developed. Prediction of the biological activity of the inhibitors has shown that the best QSAR model, can be constructed from quantum-chemical properties, heat of formation, total energy, highest occupied molecular orbital energy, lowest unoccupied molecular orbital energy and ionization potential. This finding proved that these properties are the prerequisite to describe the electronic structure of the drugs. On the basis of the derived models, one can build up a theoretical basis to access the biological activity of the compounds of the same series.

Preventing and reducing the spread of HIV (HIV) has always been a concern in medical science. One of the most common ways to control the virus is using enzyme-blocking drugs. In this study, we attempted to predict the biological activity (PKi) of organic urea derivatives in protease inhibitor compounds using molecular modeling using QSAR (Quantitative Structure Activity Relation), which is the basis of quantitative study of the structure between And there is activity. Models were presented. In this study, the chemical structure of 41 compounds was optimized by Gaussian 09 software and other properties (descriptors) were obtained using software. The level used in B3LYP calculations and ground state series was 6-31G *. Validation tests were then performed on the obtained models. The results of the statistical data were acceptable. Given the effective variables in the models, it predicts biological activity and invokes appropriate drug design.

Two data sets of natural antiviral agents including 107 anti-HIV1 and 18 anti-polio molecules were collected and subjected to quantitative structure-activity relationship (QSAR) analyses. A wide variety of molecular descriptors belonging to various structural properties were calculated for each molecule. Multiple linear regression (MLR) based on stepwise variable selection was employed to find the most convenient quantitative models. For each antiviral data set different QSAR models were established in two steps. Firstly, for each type of molecular descriptors separate QSAR analysis was performed, and then a new QSAR model was calculated using the selected descriptors in the first phase. For both types of antiviral data sets significant QSAR models were obtained. The atom-centered fragment descriptors represented the highest impact on the anti-HIV1 activity whereas for anti-polio agents, radial distribution function and three-dimensional MoRSE descriptors showed the most significant influences. Cross-validation and a separate prediction set were used to evaluate the stability and prediction ability of the models. It was found the discovered QSAR models for anti-HIV1 and anti-polio agents could reproduce about 80% and 90% of variances in the antiviral activity data with root mean square error of prediction of 0.421 and 0.171, respectively.

The anti-oxidant activities for a diverse set of flavonoids as TEAC (Trolox equivalent anti-oxidant capacity) assay were subjected to 3D-QSAR (3-dimensional quantitative structural-activity relationship) studies using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis). The obtained results indicated superiority of CoMSIA model over CoMFA model. The best CoMSIA model was developed by using hydrogen-bond donor (H-bond donor) and electrostatic field components. This model gave the cross-validated correlation coefficient, Q2 = 0. 512, correlation coefficient, R2 = 0. 950, standard error of prediction, SE = 0. 284, and F = 47. 3, for training set, and R2 = 0. 922 and SE = 0. 286, for test set indicating robustness and high prediction power of the developed model. The contour maps of electrostatic and H-bond donor fields of CoMSIA model provide interpretable and fruitful relationship between chemical structures and their anti-oxidant activities giving useful insight for designing new compounds with higher activity.

Recently several 1,3,4-oxadiazole derivatives were identified as potentially active antimycobacterial agents. Various 5-aryl-2-thio-1,3,4-oxadiazoles have been reported having good antimycobacterial activity against Mycobacterium tuberculosis H37Rv (ATCC 27294). In this paper we report 3D QSAR studies for the 41 molecules of 1,3,4-oxadiazoles by using k-Nearest Neighbor Molecular Field Analysis (kNN-MFA) combined with various selection procedures. Using kNN-MFA approach 52 3D-QSAR models were generated; one of these models was selected on the basis of q2 and pred_r2 values. The selected model had shown good internal and external predictivity for the training set of 33 molecules and test set of 8 molecules with validation (q2) and cross validation (pred_r2) values of 0.5022 and 0.2898, respectively. This model can be used for preliminary screening of large diversified compound libraries. The model has shown that presence of sulphur is must for activity, however the larger bulky substituents reduce the activity. The presence of halogen and other non-halogen groups have also contributed to the activity.Hence the future schemes with smaller groups on sulphur and electronegative groups in the molecule would result in potentially active molecules.

New phosphorhydrazide compounds (1–7, 13, 15–16) and thiophosphorhydrazide (14 and 17) were synthesized and characterized by 31P, 13C, 1H NMR and IR spectroscopy. Furthermore, the crystal structure of compound (C6H5NH)(C6H5O)P(O)(NH–NH2) (2) was investigated. The activities of derivatives on acetylcholinesterase (AChE) and urease were determined. Quantitative structure–activity relationship (QSAR) was used to understand the relationship between molecular structural features and inhibitory. DFT–QSAR models for enzymes demonstrated the importance of ELUMO parameter in describing the anti-AChE and anti-urease activities of the synthesized compounds. The correlation matrix of QSAR models and docking analysis confirmed that electrophilicity descriptor can control the influence of the polarizability properties of N–H functional group of PAH derivatives in the inhibition of enzymes.