Quantitative structure-activity relationship (QSAR) is a mathematical and computational modelling approach used to predict the biological activity of a chemical compound based on its physicochemical properties and molecular structure.
QSAR models are developed by investigating the relationship between a group of chemical compounds with known biological activity and their physicochemical characteristics, such as molecular weight, polarity, hydrophobicity, and others.
The biological activity of new drug with comparable physicochemical characteristics and molecular structures is then predicted using the models.
QSAR is frequently used to predict the toxicity, bioavailability, and other characteristics of chemical compounds in drug discovery, environmental chemistry, and other domains.
Statistical and machine learning algorithms are used to examine huge datasets of chemical compounds and their biological activities in order to create QSAR models.
These models can help with the identification of new drug candidates and the molecular structure optimization of those candidates to increase their biological activity and decrease their toxicity.
In QSAR modelling, the following physicochemical characteristics are frequently used:
1.Partition coefficient:
The logarithm of the partition coefficient (logP) is a measure of the compound's hydrophobicity. It is calculated by taking the ratio of the compound's concentration in an organic solvent to its concentration in water.
One physicochemical property that is frequently used as a predictor in quantitative structure-activity relationship (QSAR) models is partition coefficient, which is sometimes denoted as logP.
The partition coefficient (logP) is the logarithm of the ratio of a compound's concentration in a polar solvent (such as water) to that in a non-polar solvent (such as octanol).
The logP value reveals the compound's hydrophobicity or lipophilicity, which might affect how it is absorbed, distributed, and eliminated by the body.
In QSAR modelling, logP is used as a descriptor or predictor variable to establish a connection between a compound's chemical components and its biological activity.
Several approaches, such as physical observations or computer algorithms based on molecular structures, can be used to obtain the logP value.
The identification of chemical compounds with the best hydrophobicity and enhancement of their potency and bioavailability can be done by using of QSAR models that include logP values as a predictor variable.
For the purpose of drug formulation and administration, the logP value can also be used to calculate a compound's solubility in various solvents.
