Recent Trends in QSAR in Modelling of Drug-Protein and Protein-Protein Interactions

The unprecedented growth in the area of QSAR has completely changed the landscape of drug discovery. QSAR techniques quantitatively correlate the associations between chemical structure alterations and respective changes in biological activity, thereby playing a major role in improving the potency, efficacy and selectivity of the lead compounds in drug design. In this review, authors have summarized the role of QSAR in drug discovery, especially with respect to lead optimization and drug-receptor interactions. The recent trends in the usage of 3D-QSAR to understand Protein-Protein Interactions (PPIs) have been explored. Specifically, the latest advances in the concepts of chemical Space (CS) and chemography have been examined in detail. Also, the authors have tried to present the current limitations and challenges in this field. The authors agree with the prevalent view that the models must be systematically validated both internally as well as externally to strengthen the hit rates in the experiments. It is important to apply the 'in cerebro-in silico' approach that entails choosing the method specific to the target-ligand system.

Automated summary

The unprecedented growth in the area of QSAR has revolutionized drug discovery by quantitatively correlating chemical structure alterations with changes in biological activity, thus enhancing the potency and efficacy of lead compounds. Recent trends in using 3D-QSAR to understand Protein-Protein Interactions (PPIs) have been explored, along with advancements in chemical Space (CS) and chemography. Validators emphasize the importance of systematically validating models both internally and externally to improve hit rates in experiments, and stress the necessity of choosing methods specific to target-ligand systems.