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Oligonucleotide Drugs Structure-Activity Relationship Analysis

The specificity of drug action depends on the specificity of binding between the drug molecule and the receptor. The specificity depends on the chemical structure of the drug (including the basic skeleton, main configuration, active gene and side chain length), which is the structure-activity relationship of the drug. Structure-activity relationship is the core of pharmacochemistry and the basis of drug design. The design and optimization of lead compounds depend on the understanding of structure-activity relationship. Quantitative structure-activity relationship (QSAR) is a method to quantitatively study the interaction between small organic molecules and large biological molecules, and the absorption, distribution, metabolism, excretion and other physiologically related properties of small organic molecules in the body by mathematical and statistical means with the help of physical and chemical property parameters and structural parameters of molecules. Through QSAR study, Creative Biolabs can assist customers to obtain the interaction mode or features between drug molecules and biological macromolecules indirectly. 

Creative Biolabs can help customers make targeted structural modifications and screen novel emerging compounds to improve efficacy and selectivity, improve pharmacodynamic properties, reduce toxicity/carcinogenesis and avoid intellectual property issues.Creative Biolabs has rich practical experience and core technology in the research of quantitative structure-activity relationship, providing efficient and high-quality technical services for the majority of scientific researchers and companies. Welcome to contact us for more research proposals.

We provide but are not limited to:

  • 2D-QSAR. Creative Biolabs takes the overall structural properties of molecules as parameters and conducts regression analysis of molecular physiological activity, establishing the correlation model between chemical structure and physiological activity. Common 2D-QSAR methods include Hansch method, Free-Wilson method and molecular connectivity method. The most widely used is Hansch method.
    • Hansch method. The classical Hansch equation model takes the half effective quantity of physiological biological substances as the activity parameter, and the electrical, stereoscopic and hydrophobic parameters of molecules as the variables of linear regression analysis. The basic idea is to quantitatively express the activity of a drug molecule by its physicochemical parameters.
    • Free-Wilson method (de novo method).  Based on Free-Wilson method, Creative Biolabs can quantitatively express the relationship between chemical structure and biological activity of oligonucleotide drugs mathematically. This study includes the classical Free-Wilson method and its improvement, Cam-marata method and Fujita-Ban method. Using the classical Free-Wilson model, the activity contribution of substituents at one position is not affected by that at another position. The activity contribution of parent nucleus and each substituent is constant. The biological activity of the compound is the sum of these constants.
  • 3D-QSAR. Creative Biolabs introduces three-dimensional structure information of oligonucleotide drug molecules, which indirectly reflects the non-bond interaction characteristics between drug molecules and macromolecules. Compared with 2D-QSAR, 3D-QSAR has clearer physical significance and richer information. The most widely used 3D-QSAR methods are comparative molecular field assay (CoMFA) and comparative molecular similarity assay (CoMSIA). Creative Biolabs can assist in the development of a receptor-based 3D-QSAR approach. This method introduces receptor structure information, which provides more information than the traditional ligand-based 3D-QSAR method (CoMFA and CoMSIA), and directly reflects the interaction pattern between drug molecules and biomacromolecules.

References

  1. Polishchuk P. Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future. J Chem Inf Model. 2017, 57(11):2618-2639.
  2. Uesawa Y. Quantitative structure-activity relationship analysis using deep learning based on a novel molecular image input technique. Bioorg Med Chem Lett. 2018, 28(20):3400-3403.

*For Research Use Only. Not for use in diagnostic procedures.

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