Human Immunodeficiency Virus (HIV)-1 protease is one of the key targets for Acquired Immunodeficiency Syndrome (AIDS). A large number of inhibitors are being designed for this target with the focus towards interactions with backbone atoms to combat drug resistance. In the present study, we have developed QSAR models for 99 inhibitors which include P1/P1 and P2/P2 substituents with diverse scaffolds. In the present work, HIV-1 protease inhibitors dataset with tanimoto similarity of 0.7 were compiled from The Binding Database (Binding DB). Multiple linear regression analysis was performed to compute the relationship between 2D and 3D structure descriptors and binding affinity. Untransformed and non-linearly transformed descriptors were used for the QSAR model development. Transformation of descriptors resulted in better QSAR model (r2=0.77) compared to the model developed using untransformed descriptors (r2=0.74). Molecular connectivity, cosmic bond angle energy and charged based descriptor were reported as a priori properties in the prediction of binding affinity. The developed models were validated using an external test set and r2 test values of 0.73 and 0.72 were obtained. Models developed in this study have potential application in the prediction of binding affinity for the newly synthesized compounds.
Keywords: HIV-1 protease, QSAR, binding affinity, descriptors, non-linear transformation, diverse inhibitors, regression, TSAR, drug design, CoMFA