Taft’s steric parameter & Hansch analysis

• Quantitative structure-activity relationship (QSAR) modelling can make use of Taft's steric parameter, denoted Es.

• The steric parameter, which measures steric hindrance or bulkiness of a substituent group attached to a molecule, is determined by the difference between the free energy of activation for a reaction between a compound with a particular substituent group and a reference compound with a methyl group.

• The Taft steric parameter is used in QSAR modelling to create a link between a compound's molecular structure and its biological activity or other physicochemical attributes.

• The steric parameter can be calculated computationally or measured experimentally based on the molecule structure.

• By identifying the optimal substituent groups that can improve the molecule's activity, QSAR models that contain the Taft steric parameter can help in predicting the biological activity of a chemical and optimising its structure.

• The steric parameter can also be used to optimize the design of enzyme inhibitors and other bioactive compounds by identifying the optimal steric properties of the ligand that can enhance its binding affinity to the target enzyme or receptor.
  

Hansch analysis:

• Hansch analysis is a technique used in quantitative structure-activity relationship (QSAR) modelling to determine a connection between a compound's physicochemical qualities and its biological activity.

• Multiple linear regression (MLR) is used in Hansch analysis to correlate a group of chemicals' biological activity to their physicochemical characteristics, such as lipophilicity, steric hindrance, and electronic effects.

• The following equation is a representation of the MLR model:

• log (1/C or pIC50) = c + b1X1 + b2X2 + … + bnXn

• where C is the compound's concentration needed to produce a certain biological response.

• The IC50 value, or the concentration needed to block a biological activity by 50%, is expressed as pIC50.

• X1, X2, … The compounds' physicochemical descriptions are given by Xn, and the regression coefficients are given by b1, b2,..., bn.

• c is constant.

• Hansch analysis can be used to identify the physicochemical properties that are most important for the biological activity of a compound and to optimize its structure by modifying its chemical structure to enhance its activity.

• QSAR models that incorporate Hansch analysis can be used to create new compounds with improved potency and selectivity for a specific target and to forecast the activity of substances with related physicochemical qualities,

Polarizability:

• Polarizability is a measure of the ease with which a molecule's electron cloud can be distorted by an external electric field.

Dipole moment:

• A molecule's dipole moment is a measure of its polarity. It is calculated as the product of the charge on each atom and the distance between them.

Surface area:

• The surface area of a molecule is a measure of its exposed area, which can influence its interaction with other molecules.

Number of hydrogen bond donors and acceptors:

• Hydrogen bond donors and acceptors are functional groups in a molecule that can form hydrogen bonds with other molecules.

Topological parameters:

• These parameters describe the molecular structure, such as the number of rings, branches, and other features.

Aromaticity:

• Aromaticity is a property of certain organic molecules that have a cyclic arrangement of electrons that make them particularly stable.

Molecular weight:

• The molecular weight of a compound is the sum of the atomic weights of all the atoms in the molecule. It is a measure of the size of the molecule.

These physicochemical parameters are used into QSAR models to establish a connection between a compound's chemical structure and biological activity.

Application of quantitative structure activity relationship (QSAR)

Quantitative structure-activity relationship (QSAR) has many applications in the fields of chemistry, pharmacology, and toxicology. Some of the most common applications of QSAR include:

1. Drug Discovery

2. Environmental Toxicology

3. Risk Assessment

4. Regulatory Compliance

5. Material Science

6. Food and Flavour Science

Overall, QSAR is a powerful tool that can help researchers to design and optimize new compounds and assess their potential impact on human health and the environment.

Advantage’s quantitative structure activity relationship (QSAR)

some of the advantages of QSAR are –

1. Time and Cost-Efficient

2. Prediction Accuracy

3. Reduced Need for Animal Testing

4. Improved Compound Design

5. Better Understanding of the Mechanisms of Action

Disadvantages quantitative structure activity relationship (QSAR)

While QSAR has many advantages, it also has some potential disadvantages, including –

1. Limited Applicability

2. Data Availability:

3. Lack of Transparency

4. Limited Understanding of Biological Mechanisms

5. Dependence on Assumptions