The correlation of structural features with the biological activity has always played an important role in drug designing process. The present paper discussesthe 2D†and 3D†Quantitative structure activity relationship (QSAR) studies, performed on a series of compounds related to saquinavir, an established HIVâ€protease inhibitor (PI). The analysis was done on structure based calculations using various methods of QSAR like multiple linear regression (MLR), kâ€nearest neighbour (kâ€NN) and partial least square (PLS), to establish QSAR models for biological activity prediction of unknown compounds. A total of 27 peptidomimetics (Saquinavir analogues) were used for the study and models were developed using a training set of 22 compounds and test set of 5 compounds. The r2 value of 0.959 and crossvalidated r2 (q2) of 0.926 was obtained when models were generated using physicochemical descriptors during 2Dâ€QSAR analysis. In case of 3Dâ€QSAR analysis, database alignment of all compounds was done by field fit of steric and electrostatic molecular fields. 3Dâ€QSAR models generated showed r2 of 0.81 when steric and electrostatic fields were considered as basis of model generation. The meaningful information obtained from the study can be used for the design of saquinavir analogues having better inhibitory activity for HIVâ€protease. Also, the QSAR models generated can be very useful to predict the HIVâ€PIs and also for virtual screening for identification of new lead molecules.
Inhibition activity prediction for a dataset of candidates’ drug by combining fuzzy logic with MLR/ANN QSAR models