Group‐Based QSAR (G‐QSAR): Mitigating Interpretation Challenges in QSAR

Several approaches are widely being used as important tools for drug discovery. These approaches include Hansch method, Free-Wilson method and conventional 2-D/3-D QSAR methods. The Hansch analysis assumes that substituents are independent of each other and does not include explicit interactions of groups. In the conventional QSAR method, the interpretation of model generated is rather difficult, as one does not a get clear direction about the site for improvement. A new Group-Based QSAR (G-QSAR) method is proposed which uses descriptors evaluated for the fragments of the molecules generated using specific fragmentation rules defined for a given dataset. Herein, we describe the application of G-QSAR method on two different datasets belonging to a simple congeneric series and a complex noncongeneric series. This method provides models with predictive ability similar or better to conventional methods and in addition provides hints for sites of improvement in the molecules.