Pharmacophore

New Chemical Entities (NCEs) were designed using information from pharmacophore profile of known anticonvulsants. Binding affinities of designed NCEs were studied on gamma-aminobutyric acid (GABA-alpha) using docking studies. Two Dimensional (2D) Quantitative Structure–Activity Relationship (QSAR) studies were performed for correlating the chemical composition of triazolethione analogs and estimation of their anticonvulsant activity using Multiple Linear Regression (MLR) Analysis.  ADMET properties were also predicted. These four basic strategies (pharmacophore mapping, QSAR, docking & ADMET screening) were implemented to evaluate the performance of derivatives. Although predicted Ki through QSAR model showed significant mild activity for GABA. Conclusively compounds, 2, 6, 7, 8, 9, 10, 11, and 20 were observed to be most feasible to activate GABA for anticonvulsant activity.

In recent years, the use of computers in chemistry have been developed greatly. The molecular docking, computer based drug designing, etc. has proved the efficiency of computers in computational chemistry. With the use of sophisticated computational tools and techniques the drugs discovery has been accelerated in the form of target based drug discovery rather than rational methods. In the current scenario the software used for drug designing are very expensive, so in this paper the power of internet and open source community has been used for performing SAR and Pharmacophore based drug design approaches as they are free to use. Using SAR and Pharmacophore study here some derivatives of Podophyllotoxin have been designed by understanding their toxicity, metabolic sites and drug like properties.

In the present study a series of 20 histone deacetylase 8 (HDAC8) inhibitors were used for generation of pharmacophore. A five features pharmacophore with one hydrogen bond acceptor (A), two hydrogen bond donors (DD) and two ring aromatics (RR) was developed and used for searching compound database. A statistically significant atom based 3D QSAR model was built by selecting best pharmacophore hypothesis ADDRR.3 with R2 = 0.9821 for training set of 14 molecules and Q2= 0.7314, RMSE= 0.1709, Person-R= 0.9061 for test set of 6 molecules. These parameters indicate that the model is a good predictive model. Docking study of known inhibitors as well as hits resulted after data base searching having fitness score ≥ 1 was performed. Docking analysis shows the important residues in the active site of receptor are Zn-388, TYR-306, HIS-142, PHE-152, PHE-208, GLY-151, and HIE-180. The XP glide score of highest active compound 1 and lowest active 20 are -11.73 and -6.036 respectively, which corroborated with experimental activity. On the basis of pharmacophore matching, predicted activity and docking interactions 5 novel chemical scaffold (Code No: CACPD2011a-0001275680, CACPD2011a-0000573705, CACPD2011a-0001843791, CACPD2011a-0000300107, CACPD2011a-0000291783) are reported as potent HDAC8 inhibitors.