Structure-activity relationship (SAR) explores the relationship between a molecule's biological activity and its three dimensional (3D) structure of the molecule. Creative Biolabs provides tailored SAR and QSAR model building services to accelerate your drug discovery process.

Generally, if the structure of a hit is known, the biological effects of the hit are predicted using other similar compounds' data. This is on the basis that structurally similar compounds may have similar physical and biological properties. Computational chemistry and molecular modeling softwares are adopted as effective tools in identifying binding site interactions.

SAR is valuable information in drug discovery and development. It is applied for discovering and developing new compounds, as well as assessing potential health risks posed by existing compounds. For instance, the analysis of SAR enables the determination of which chemical groups play an important role in evoking a target effect in the organism. This determination allows rationally modification of the effect or improving the potency of a bioactive compound by changing its chemical structure or insert new chemical groups. In the case of risk assessment, similar data from the most sensitive toxicological endpoints can be used such as carcinogenicity or cardiotoxicity.

Quantitative SAR (QSAR) model is regarded as a special case of SAR (when relationships become quantified), and this model relates a set of "predictor" variables (X) to the potency of the response variable (Y) to predict the activity of chemicals. The unique methods allow researchers to go beyond merely characterizing structures as "active" or "inactive", but predict the level of biological activity or potency.

sar and qsar models Figure 1. Overview of QSAR/QSPR modeling (Patel et al. 2014)

QSAR are categorized into six classes based on predictor variables:

  • 1D-QSAR: correlating molecular activity with molecular properties like pKa, log P, etc.
  • 2D-QSAR: correlating activity with structural 2D patterns like connectivity indices, 2D-pharmacophores.
  • 3D-QSAR: correlating activity with non-covalent interaction fields surrounding the molecules.
  • 4D-QSAR: additionally including ensemble of ligand configurations in 3D-QSAR.
  • 5D-QSAR: explicitly representing different induced-fit models in 4D-QSAR.
  • 6D-QSAR: further incorporating different solvation models in 5D-QSAR.

Creative Biolabs has developed several QSAR techniques to help you obtain the best QSAR models for hit to lead process.

Comparative Molecular Field Analysis (CoMFA)

It was the first used 3D-QSAR method and has served as a well-deserving tool for decades. The steric and electrostatic values are amended using cut-offs (±30 kcal/mol), depending upon the position of the lattice point. CoMFA generates an equation correlating the biological activity with interactive energy field's contribution at every grid point.

Comparative Molecular Surface Analysis (CoMSA)

CoMSA is a non-grid 3D-QSAR approach that makes use of the molecular surface for labeling specific areas defined on the molecular surface using the mean electrostatic potentials. In this method, the molecules are subjected to the data set to geometry optimization and assigning them with partial atomic charges.

Hologram QSAR (HQSAR)

HQSAR is a 2D QSAR method and conducted a series of structurally diverse molecules with known PPB. The models were used to predict fragment-based structure-activity relationships which exhibiting a powerful predictive capability. It is useful for the further design of novel, structurally related drugs.

Creative Biolabs streamlines the organization of QSAR datasets, QSAR models, and QSAR predictions. With our one-stop service, you can work more efficiently and effectively. For more detailed information, please feel free to contact us or directly sent us an inquiry.

Reference

  1. Patel H M, Noolvi M N, Sharma P, et al. (2014) "Quantitative structure–activity relationship (QSAR) studies as strategic approach in drug discovery." Med Chem Res 23: 4991. doi:10.1007/s00044-014-1072-3

For lab research use only.



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