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)
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.