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In the current scenario, QSAR has become a multidimensional field of study with
the development of more sophisticated approaches such as 4D-QSAR [52], 5D-QSAR
[53], and 6D-QSAR [54].
The major historical events in the development of the QSAR are given in Tab. 2.1.
Tab. 2.1: Historical development of the QSAR.
S. no. Name of
contributor (year)
Major contribution/development
. Fraser and
Crum-Brown []
Postulated that there exists a correlation between the physiological
action and the chemical constituent
. Richet [] Found an inverse relationship between the toxicity and molecular weight
of the organic compounds
. Overton [] and
Meyer []
Postulated that the toxicity of narcotic compounds to the tadpoles and
other aquatic species can be correlated to their lipophilicity
. Longinescu [] The first attempt to correlate the melting point with the structure of the
compound was made
. Fuhner [, ] Established that within the homologous series of the compounds, the
narcotic toxicity follows geometric progression which suggests additivity
of group contributions
. Hammet [] Developed a correlation between the dissociation constant and
substitution on the benzene ring
. Ferguson [] A thermodynamic interpretation was given for the toxic effects of certain
compounds when correlated with the chemical constitution
. Taft [] Gave a linear relationship between the log of the rate of hydrolysis of
substituted benzamides and electronic steric factors
. Zahradnik [] Extension of Hammet’s equation to the biological system
. Hansen [] Provided the real correlation between the toxicity of substituents and
their steric constants (σ) and considered as a true extension of the work
of Hammet
. Hansch and Fujita
[]
Provided the linear free energy relationship, also known as Hansch
analysis
. Free and Wilson
()
Gave the molecular fragment-based QSAR study, popularly known as the
Free Wilson approach
. Fujita and Ban [] Gave a mathematical expression to the Free Wilson approach
. Fukunaga [] The first D-QSAR model was generated for the inhibition of
dihydrofolate reductase enzyme
2.3 History and development of QSAR 29
Over the years, QSAR has found applications in various fields, including pharmaceut-
icals, environmental science, agrochemicals, and material design. In drug discovery,
QSAR is instrumental in identifying lead compounds with desired pharmacological
properties, optimizing their structures for enhanced efficacy, and predicting potential
toxicity or side effects. In environmental science and agrochemicals, QSAR aids in
evaluating the ecological and toxicological risks of chemicals, enabling better deci-
sion-making for sustainable development. Additionally, QSAR is employed in design-
ing innovative materials with specific properties, contributing to advancements in
nanotechnology and materials science.
Despite its numerous successes, QSAR does face certain challenges and limita-
tions. The accuracy of QSAR models heavily relies on the quality and representative-
ness of the data used for training. Moreover, extrapolating QSAR model s to predict
activity against new, previously unseen compounds requires cautious validation. As
with any computational approach, overfitting and underfitting are potential issues
that need to be carefully addressed. In conclusion, QSAR is a pivotal field that has
transformed the landscape of drug discovery and various other scientific domains. By
combining chemistry, biology, and computational techniques, QSAR provides a sys-
tematic and rational approach to understanding and predicting the activity of mole-
cules. As technology continues to evolve, QSAR is expected to play a n eve n mor e
significant role in shaping the future of drug design and the development of innova-
tive chemical compounds with diverse applications.
Tab. 2.1 (continued)
S. no. Name of
contributor (year)
Major contribution/development
. Kubiyi [] A bilinear model for the nonlinear dependence of biological activity on
the hydrophobic character was proposed, also known as a mixed
Hansch/Free Wilson model
. Cramer [] A linear D-QSAR model, comparative molecular field analysis (CoMFA),
was generated
. Klebe [] A variant of CoMFA, comparative molecular similarity analysis indices
(CoMSIA), was generated
. Hopfinger [] Generated D-QSAR model
. Vedani [] Introduction of D-QSAR model generation was provided
. Vedani [] The first D-QSAR model was introduced
30 Chapter 2 Quantitative structure–activity relationship
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References 31
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32 Chapter 2 Quantitative structure–activity relationship
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References 33
Chapter 3
Physicochemical parameters and QSAR
Molecular descriptors are the language through which QSAR translates chemical structures into
predictive models. – R. Todeschini and V. Consonni
3.1 Physicochemical parameters and QSAR
Quantitative structure–activity relationship (QSAR) is a computational modeling tech-
nique used in chemistry, pharmacology, and toxicology to predict the biological activity
or properties of molecules based on their structural characteristics [1]. By analyzing the
relationship between molecular structures and experimental activity data, QSAR mod-
els can estimate the potency, toxicity, or other relevant properties of new compounds
without expensive and time-consuming laboratory tests [2, 3]. Utilizing mathematical
algorithms, descriptors, and statistical methods, QSAR studies enable the identification
of promising drug candidates, environmental pollutants, and chemical compounds, ac-
celerating drug discovery, risk assessment, and regulatory decision-making processes
[4]. Despite its advantages, the accuracy and reliability of QSAR models heavily depend
on the quality and diversity of the available data [5].
Physicochemical properties play a crucial role in determining the behavior and
interactions of molecules in various environments [6]. Some of the important physico-
chemical properties commonly considered in QSAR studies include:
– Molecular weight: The total atomic weight of a molecule, which is essential in
understanding its mass and size.
– Partition coefficient (log P or log D): A measure of a compound’s tendency to
partition between a hydrophobic (lipid) phase and a hydrophilic (aqueous) phase.
It provides insight into the compound’s solubility and permeability.
– Hydrophobicity: Related to the partition coefficient, it describes the tendency of
a compound to avoid water interactions and prefer hydrophobic environments.
– Polarizability: A measure of how easily the electron cloud of a molecule can be dis-
torted by external electric fields, influencing its interactions with other molecules.
– Electrostatic charge: The distribution of electric charge in a molecule affects its
interactions with charged entities, such as proteins or ions.
– Hydrogen bond donor/acceptor count: The number of hydrogen bond donors
and acceptors in a molecule impacts its ability to form hydrogen bonds, affecting
various biological interactions.
– Topological polar surface area: A measure of the accessible surface area of a
molecule that is polar and, therefore, potentially involved in interactions with bi-
ological targets.
https://doi.org/10.1515/9783111434858-003
– Refractivity/polarizability: Another measure related to molecular polarizability,
providing information about the compound’s refractive index.
– Molecular flexibility: Describes the ability of a molecule to undergo conforma-
tional changes, influencing its binding and activity.
– Steric parameters: Parameters that account for spatial arrangements of atoms
and groups within the molecule, impacting its interactions with other molecules.
– Dipole moment: The measure of the overall molecular polarity, which is impor-
tant in understanding molecular interactions.
– Ionization constant (pK
a
): The measure of acidity or basicity of a compound, af-
fecting its behavior in different pH environments.
These physicochemical properties, among others, are used as descriptors in QSAR
models to establish relationships between molecular structures and biological activi-
ties. By building predictive models based on these relationships, QSAR allows for the
estimation of various properties and biological activities of compounds, which can be
valuable in drug design, environmental assessment, and other fields of research.
The very first question that should come to mind is why physicochemical properties
are so important in a QSAR study. The basic explanation to this question is that the origin
of the physicochemical properties is due to the electronic arrangement of the molecule
and its interaction with the biological system. Hence any change in the physicochemical
property of a drug candidate will directly influence its biological response. For the suc-
cessful development of any mathematical relationship, it is indispensable to study the
changes in the physicochemical properties of the drug candidates. Types of basic physico-
chemical properties that influence biological activities are given in Tab. 3.1.
3.1.1 Lipophilicity parameters
The term lipophilicity is often used interchangeably with hydrophobicity, as both have
the same meaning. Lipophilicity parameters are most studied and exploited for defining
the QSAR studies [7]. To define the hydrophobicity/lipophilicity parameters of a drug
Tab. 3.1: Types of physicochemical parameters employed in QSAR model generation.
S. no. Physicochemical
parameters
Examples
. Lipophilicity
parameters
Partition coefficient, distribution coefficient, lipophilic substituent
constituent, chromatographic parameters
. Electronic parameters Hammett constant, dipole moments, field and resonance parameters
. Steric parameters Molar refractivity, parachor, molar volume, Taft’s steric factor,
Verloop steric factors
3.1 Physicochemical parameters and QSAR 35
molecule and its dependence on biological activity, the following parameters are stud-
ied and measured:
3.1.1.1 Partition coefficient (P)
Partition coefficient in the simplest terms is defined as the ratio of the concentration
of unionized drugs in organic and aqueous solvents at equilibrium by the following
equation:
P =
Concentration in an organic solvent
Concentration in aqueous solvent
(3:1)
This is an important physicochemical parameter as it is an indicator of the ability of a
drug to cross the cell membrane [8]. For QSAR studies, the octanol/water system is pre-
ferred due to its high resemblance with the biological system [7]. The therapeutic effect
of any drug is possible only if it reaches the systemic circulation after crossing the cellu-
lar membrane, which is lipophilic in nature. This journey of the drug molecule inside
the biological system can be understood by a simple kinetic model that has been de-
scribed in Fig. 3.1. A lipid compartment (representing the cell membrane) lies between
two aqueous compartments. Any compound that has a low P value (hydrophilic mole-
cule) will remain in the first compartment and will not cross the cell membrane. Like-
wise, compounds possessing high P value (lipophilic molecule) will be retained in the
lipid compartment. Hence, an optimal value of P is desirable for a drug to cross the cell
membrane, which in turn indirectly corresponds to its biological activity.
From the earlier studies, a linear relationship has been established between the
biological activity and the partition coefficient of the drug molecules (Fig. 3.2). These
earlier studies were done on small homologous series having a small range of P val-
ues [9]. The equation which emerged from these studies is given below:
log
1
C
= a log P + b (3:2)
where C represents the concentration required for producing the biological response,
P is the partition coefficient, and a and b are constants. This equation resembles the
equation of a straight line (y = mx + c).
Hansch with his coworkers suggested that with a large range of P values, rather
than linear relationships, there exists a parabolic relationship between the biological
activity and P value [10] (Fig. 3.3).
They enumerated the following equation:
log
1
C
= −k
1
log PðÞ
2
+ k
2
ðlog PÞ
2
+ k3(3:3)
where C is the concentration for producing a biological response, P is the part ition
coefficient, and k
1
, k
2
, and k
3
are constants obtained through the least square method.
36 Chapter 3 Physicochemical parameters and QSAR
Fig. 3.1: Dependence of the partition coefficient on the absorption of the drug. There should be an optimum partition coefficient of the drug. In
drugs having a high partition coefficient, the drug is hydrophobic and will remain in the lipophilic membrane whereas in drugs with a low
partition coefficient, the drug is hydrophilic and it will remain in the GIT.
3.1 Physicochemical parameters and QSAR 37
Fig. 3.2: Relationship between the log P (partition coefficient) value and the biological activity (log(1/C)) of
the drug for a small range of the log P values.
Fig. 3.3: Relationship between the log P (partition coefficient) value and the biological activity (log(1/C)of
the drug for a small range of the log P values. P
0
is the optimum value of the partition coefficient where
the maximum biological response is obtained.
38 Chapter 3 Physicochemical parameters and QSAR
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