원문정보
초록
영어
Quantitative structure-activity relationship (QSAR) methodologies have been applied for many years, to correlate the relationship between physicochemical properties of chemical substances and their biological activities to generate a statistical model for prediction of the activities of new chemical entities. The basic principle behind the QSAR models is that, how structural variation is responsible for the difference in biological activities of the compounds. 3D- QSAR has emerged as a natural extension to the classical Hansch and Free-Wilson approaches, which develops the 3D properties of the ligands to predict their biological activities using various chemometric techniques (PLS, G/PLS, ANN etc). It has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. This review seeks to provide different 3D-QSAR approaches involved in drug designing process to develop structure-activity relationships and also discussed the fundamental limitations, as well as those that might be overcome with the improved methodologies.
목차
1. Introduction
2. Experimental Section
2.1. Objectives of QSAR
2.2. Classification of QSAR Methodologies
2.3. Assumption in 3D-QSAR Methods
2.4. Classification of 3D-QSAR Approaches CoMFA
2.5. GRID
2.6. CoMSIA
2.7. COMBINE
2.8. Statistical Methods Used for Building QSAR Models
2.9. Validation of 3D-QSAR Models
3. Conclusion
3.1. Concluding Remarks
References