Quantitative Structure–Activity Relationship (QSAR) is a scientific method used to relate the chemical structure of a molecule to its biological activity using mathematics and statistics. In simple terms, QSAR helps predict how a chemical compound will act in the body based on its structure. It plays a major role in drug discovery, toxicology, and medicinal chemistry.
What is QSAR?
QSAR is built on a simple idea: if two molecules have similar structures, their biological effects will also be similar. By analyzing structural features and physicochemical properties, QSAR models can predict whether a new molecule will be active, inactive, toxic, or safe—without needing to test it first in the lab.
Why QSAR is Important
- Speeds up drug discovery by reducing the number of compounds that need to be tested.
- Predicts potency, toxicity, and safety of new molecules.
- Reduces cost of drug development by identifying promising leads early.
- Used in environmental and chemical safety evaluations.
General QSAR Equation
The basic QSAR model expresses activity as a function of molecular properties:
Log (1/c) = k₁(log P) + k₂(σ) + k₃(E) + constant
Where:
- Log (1/c) – Biological activity
- Log P – Lipophilicity
- σ – Electronic properties
- E – Steric (size/shape) effects
- k₁, k₂, k₃ – Regression constants
Key QSAR Parameters
1. Lipophilic Parameters
These describe how a molecule distributes between water and lipid layers. The most common parameter is Log P (partition coefficient). Lipophilicity influences membrane penetration, absorption, and CNS entry.
2. Electronic Parameters
Measure electron distribution in a molecule. The widely used measure is the Hammett constant (σ). Electron-withdrawing groups have positive σ values, while electron-donating groups have negative σ.
3. Steric Parameters
Describe the influence of molecular size and shape on activity. A common measure is Taft’s steric constant (Es), which explains how bulky groups affect binding.
4. Polarizability Parameters
These quantify how easily the molecule’s electron cloud can be distorted. Molar refractivity (MR) is commonly used.
5. Miscellaneous Parameters
- Molecular weight
- Hydrogen bonding features
- Topological indices
Common QSAR Models
1. Hansch Analysis
One of the earliest and most widely used QSAR models. It combines lipophilic, electronic, and steric factors to predict activity. Useful for drugs like beta-blockers and antibacterial agents.
2. Free–Wilson Analysis
Focuses purely on the contribution of substituents to activity, without considering physicochemical parameters. Very useful for analyzing substituent patterns in analog series.
3. Mixed QSAR Approach
Combines both Hansch and Free–Wilson methods to provide a more complete model.
4. Fujita–Ban Model
An advanced extension of Free–Wilson analysis that predicts activity of the parent compound even without substituents.
5. Topliss Decision Tree
A practical, non-mathematical tool that helps medicinal chemists choose substituents logically when optimizing lead compounds.
Applications of QSAR
- Drug design: Predicts potency and optimizes lead compounds.
- Toxicity prediction: Helps identify hazardous chemicals before testing.
- Regulatory use: Safety assessment for industrial chemicals.
- Pharmacokinetics: Predicts solubility, absorption, and metabolism trends.
Detailed Notes:
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