Structure Activity Relationship (SAR) Analysis Service

    Structure-activity relationship (SAR) technology plays an increasingly important role in dealing with large and ever-expanding data sources. SAR techniques are widely used, including: the computational simulation design of virtual chemical libraries to explore molecular diversity for subsequent synthesis and screening; screening proprietary, commercial, and public databases to discover lead compounds; and mining gene expression data from microarray experiments for target identification. SAR analysis can detect functional groups that have biological effects on organisms. These functional groups promote the modification of bioactive compounds by altering their chemical structure. Chemists use advanced chemical synthesis techniques to introduce new chemical groups into biomedical compounds and test the modifications for impacts on the biological functions of the compounds. This can also be improved to establish mathematical relationships between chemical structures and biological activities, known as quantitative structure-activity relationships (QSAR). The fundamental assumption of SAR analysis is that similar molecules possess similar functions. Therefore, a potential problem is how to define small differences at the molecular level, as each type of activity (such as reactivity, solubility, target activity) may also relate to another type of difference.


    Statistical Methods for SAR Analysis 

    1. Multivariate Linear Regression (MLR)

    2. Principal Component Analysis

    3. Artificial Neural Networks (ANN)

    4. Support Vector Machines (SVM)



    1. Decrypting SAR

    2. Detailed Pharmacophore

    3. Annotating Key Activity Changes

    4. SAR Data Interpretation

    5. Effective 3D-QSAR Model

    6. Actual Number of Compounds Screened

    7. Molecular Optimization to Achieve Ideal Activity and Appropriate Functionality


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