QSAR Analysis

For targets with little structural information, a ligand-based approach using the quantitative structure-activity relationship (QSAR) model may provide guidance for drug design. QSAR is a machine learning process used to develop meaningful associations between independent variables (such as molecular descriptors and structural features of compounds) and dependent variables (such as biological activity). Creative Biostructure offers QSAR analysis, which can directly reflect the relationship between the biological activity of inhibitors/agonists and their chemical structure, and explore significant factors associated with the activity of drug molecules, thereby guiding drug discovery programs.

At Creative Biostructure, we are able to provide currently widely utilized 3D-QSAR research approaches, including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). We can also support other 3D-QSAR strategies, such as Topomer CoMFA, comparative binding energy (COMBINE), comparative molecular surface analysis (CoMSA), comparative residue interaction analysis (CoRIA), and hologram quantitative structure-activity relationship (HQSAR). To make up for the defects of 3D-QSAR, more advanced multi-dimensional QSAR strategies can be adopted such as 4D-, 5D- and 6D-QSAR.

 Figure 1. A schematic of QSAR-based drug discovery. (Cherkasov A.; et al. 2014)

Our QSAR Analysis can help you:

• Establish a quantitative relationship between the chemical structure and biological activity of the compound.
• Predict the activity of the new compound based on the structural data and speculate the possible mechanism of action.
• Optimize the structure of existing compounds to enhance their biological activity.
• Design novel candidate lead compounds.
• Predict the absorption, distribution, metabolism, excretion, toxicity, and other properties of compounds.

Capabilities and advantages of our QSAR Analysis services:

• Our comprehensive computer-aided drug design (CADD) platform constantly integrates new descriptors and machine learning algorithms.
• To generate a reliable QSAR model, appropriate compounds are selected to construct the training set and test set.
• The model is validated using internal or external validation and the resulting QSAR model can be continuously optimized and validated.
• We can apply QSAR to ligand-based de novo drug design.
• We utilize the QSAR model to guide in silico virtual screening and ADME/Tox prediction.

QSAR analysis is a critical step in the optimization process of lead compounds to correlate molecular structure with biological and pharmaceutical activities. Creative Biostructure's QSAR analysis services will help scientists worldwide with drug discovery needs to develop reliable QSAR models. The QSAR model generated by us can be utilized to guide future studies. Moreover, our QSAR analysis encourages the use of high-quality, validated QSARs to regulate decision making and provide support for experimental research design.

References

1. Golbraikh A.; et al. Predictive QSAR modeling: methods and applications in drug discovery and chemical risk assessment. Handbook of computational chemistry. Springer Netherlands. 2012: 1309-1342.
2. Cherkasov A.; et al. QSAR modeling: where have you been? Where are you going to?. Journal of medicinal chemistry. 2014, 57(12): 4977-5010.
3. Vaidya A.; et al. Quantitative structure-activity relationships: a novel approach of drug design and discovery. Journal of Pharmaceutical Sciences and Pharmacology. 2014, 1(3): 219-232.