Introduction of QSAR
Structure-activity relationship (SAR) analysis can determine the main chemical groups that cause the biological reactions of drug molecules, and modify the chemical structure to improve its biological activity. Medicinal chemists use chemical synthesis and computational drug design techniques to insert new chemical groups or change existing groups in biologically active molecules, and evaluate their biological responses. SAR information is mainly obtained by experimentally confirming that the biological activity of compounds changes with the structure. SAR assumes that molecules with similar structures have similar activities, because similar compounds may have similar chemical or physicochemical properties.
Quantitative structure-activity relationship (QSAR) is an important chemometric tool in the calculation and design of drugs, and it is a common practice in ligand-based virtual screening. QSAR is the mathematical relationship between chemical structure and biological activity. The basis of the SAR assumption is that molecules with similar structures have similar functions (activity), while QSAR assumes that the biological activities (similar activities) of different molecules can be quantitatively compared based on the characteristics of their structural components. QSAR analysis provides information on structural features and/or physicochemical properties of structurally similar molecules related to their biological activities.
Introduction of 2D-QSAR
2D-QSAR is a drug design approach that utilizes the overall structural properties of the molecule as parameters. It is applied to perform regression analysis on the physiological activity of the molecule, and establish a model of the correlation between chemical structure and physiological activity. 2D-QSAR models are used routinely during the process of optimization of a chemical series towards a candidate for clinical trials. The research on two-dimensional QSAR focuses on two directions: the improvement of structural data and the optimization of statistical methods. Commonly applied 2D-QSAR methods include the hansch method, free-wilson method, molecular connection method, etc.
Our 2D-QSAR Analysis Process
- Data set preparation
- Structure optimization
- Calculation and selection of molecular descriptors
- Establishment of related 2D-models
- Evaluation and verification
Figure 1. General steps involved in QSAR modeling. (Abuhammad, A.; Taha, M. O. 2016)
Our 2D-QSAR Services
At Alfa Chemistry, our groups are capable of developing a 2D-QSAR model based on individual, estate numbers, structural, electro topological and baumann alignment descriptors parameters. Our fast and high-quality services include the following:
- We draw a molecular graph containing topological or two dimensional information, which describes how the atoms are bonded in a molecule, both the type of bonding and the interaction of particular atoms (e.g. total path count, molecular connectivity indices etc.).
- Alfa Chemistry supports density functional theory (DFT) method and molecular holographic quantitative structure-activity relationship method in 2D-QSAR.
- Our experts have rich experience in using numerical descriptors to translate a chemical structure into mathematical variables, ensuring the quality of the observed data.
- We can apply a diversity of statistical methods to accurately derive the relationships between the observations and the descriptors.
Features of Our 2D-QSAR
- Accurate fitting of the data
- Domain applicability to new structures
- Make good error estimates for each prediction
Alfa Chemistry's Advantages
- Fast screening speed and good versatility
- Fully consider both water and solvation effects
- High-performance server
- Compound database compliant with predefined filtering rules
2D-QSAR is used to design new molecules and to predict their bioactivity using the developed models. Our 2D-QSAR services remarkably reduce the cost, promote further experiments, and accelerate the process of drug design for customers worldwide. Our personalized and all-around services will satisfy your innovative study demands. If you are interested in our services, please don't hesitate to contact us. We are glad to cooperate with you and witness your success!
Reference
- Abuhammad, A.; Taha, M. O. QSAR studies in the discovery of novel type-II diabetic therapies. Expert Opinion on Drug Discovery. 2016, 11(2): 197-214.