Structure Activity Relationship techniques are currently employed in a wide range of applications, including: In-silico design of virtual chemical libraries that explore molecular diversity for subsequent synthesis and screening; screening proprietary, commercially available, and public databases for lead discovery; and, mining gene expression data from microarray experiments for target identification. It is obvious from these examples that SAR technology now fulfills expanding roles in handling large and expanding sources of data. The analysis of SAR allows the detection of the functional group which has biological effect on the organism, which facilitates the modification of a bioactive compound by changing its chemical structure. Chemists use advanced techniques of chemical synthesis to introduce new chemical groups into the biomedical compound and test the effect of modifications on their biological functions. This method was refined to build mathematical relationships between the chemical structure and the biological activity, known as quantitative structure activity relationships (QSAR). The basic assumption underlying SAR analysis is that similar molecules have identical functions. The underlying problem is therefore how to define small differences on the molecular level, since each kind of activity, e.g. reaction ability, solubility, target activity, and may also related to another difference.
Figure 1. Three level of approach to characterize chemical compounds
Statistical Methods for SAR Analysis
- Multiple Linear Regression (MLR)
- Principal Component Analysis
- Artificial Neural Networks (ANN)
- Support Vector Machine (SVM)
All of the below are included in our report.
- Decipher SAR
- Detailed pharmacophores
- Key activity changes highted
- SAR data interpretation
- Effective 3D-QSAR models
- Virtually screen numbers of compounds on your desktop
- Molecules optimization to fulfill the ideal activity and suitable function
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