Article Citation:

Kores. J. J. (2025). Deciphering the Antioxidant and Therapeutic Potential of

Lamiaceae Phytochemicals: Insights from Density Functional Theory and In

Silico Approaches. Journal of Research in Biology 15(4): 1-15

Journal of Research in Biology

Deciphering the Antioxidant and Therapeutic Potential of

Lamiaceae Phytochemicals: Insights from Density Functional

Theory and In Silico Approaches

Keywords:

Lamiaceae; Density Functional Theory; Antioxidants; Molecular Docking;

Phytochemicals; QSAR; ADMET; In Silico Drug Discovery

ABSTRACT:

The Lamiaceae family represents one of the most pharmacologically

signiﬁcant groups of medicinal plants, comprising over 7,000 species

distributed across diverse ecological regions. These plants are rich in

structurally diverse phytochemicals, including phenolic acids, ﬂavonoids, and

terpenoids, many of which exhibit potent antioxidant, anti-inﬂammatory,

antimicrobial, antiviral, and anticancer activities. In recent years,

computational chemistry particularly Density Functional Theory (DFT)

combined with in silico methodologies such as molecular docking, molecular

dynamics simulations, Quantitative Structure–Activity Relationship (QSAR)

modeling, and ADMET proﬁling, has signiﬁcantly advanced the mechanistic

understanding of these bioactive compounds.

This review systematically examines the application of DFT and

complementary computational techniques in elucidating the antioxidant

mechanisms and therapeutic potential of key Lamiaceae phytochemicals,

including rosmarinic acid, lauteolin, carnosic acid, thymol, carvacrol, and

ursolic acid. Particular emphasis is placed on quantum chemical descriptors

such as Bond Dissociation Enthalpy (BDE), Ionization Potential (IP), Proton

Anity (PA), and Frontier Molecular Orbital (FMO) energies, which govern

radical scavenging activity. Additionally, the integration of DFT-derived

descriptors with molecular docking and ADMET predictions is discussed to

highlight multi-target drug discovery potential.

Despite substantial progress, challenges remain in accurately modeling

solvent eects, conformational ﬂexibility, and biological environments. Future

directions include the integration of machine learning with quantum chemical

descriptors and the development of multi-target therapeutic frameworks. This

review provides a consolidated and critically evaluated foundation for

advancing computational phytochemistry in Lamiaceae-based drug discovery.

1-15 | JRB | 2025 | Vol 15 | No 4

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www.jresearchbiology.com

Journal of Research in Biology

An International

Scientiﬁc Research Journal

Author:

J. Jebasingh Kores

Institution:

Department of Physics,

Pope's Colleg

(Autonomous),

Sawyerpuram 628 251,

Tamil Nadu, India

Corresponding author:

Kores. J. J.

Web Address:

http://jresearchbiology.com/

documents/RA0902.pdf

Dates:

Received: 25 Aug. 2025 Accepted: 25 Nov. 2025 Published: 15 Dec. 2025

Journal of Research in Biology

An International Scientiﬁc Research Journal

ISSN No: Print: 2231 –6280; Online: 2231- 6299

Systematic Review

Kores et al., 2025

2 Journal of Research in Biology (2025) 15(4): 1-15

1. Introduction

1.1 Botanical and Phytochemical Overview of

Lamiaceae

The Lamiaceae (mint family) is one of the largest and

most economically and pharmacologically important

families of flowering plants, comprising approximately

7,000–7,500 species across more than 230 genera.

Members of this family include widely utilized medicinal

and culinary herbs such as Rosmarinus officinalis

(rosemary), Salvia officinalis (sage), Origanum vulgare

(oregano), Thymus vulgaris (thyme), Mentha species

(mint), and Ocimum basilicum (basil) .

Lamiaceae species are characterized by their rich

production of secondary metabolites, particularly:

Phenolic acids (e.g., rosmarinic acid, caffeic acid),

Flavonoids (e.g., luteolin, apigenin, quercetin),

Terpenoids (e.g., ursolic acid, carnosic acid), and

Essential oil constituents (e.g., thymol, carvacrol)

These compounds contribute significantly to the

biological activities of Lamiaceae plants, including

antioxidant, anti-inflammatory, antimicrobial, and

anticancer effects. Among these, phenolic compounds

play a dominant role in antioxidant activity due to their

ability to donate hydrogen atoms or electrons, stabilizing

reactive oxygen species (ROS). Analytical studies using

HPLC-DPPH assays have confirmed strong radical

scavenging activity in Lamiaceae-derived phenolics,

particularly in rosemary, sage, and oregano (Damašius et

al., 2014).

1.2 Importance of Computational Approaches in

Phytochemical Research

Traditional experimental approaches for evaluating

phytochemical activity, although essential, are often time-

consuming, resource-intensive, and limited in

mechanistic resolution. Computational chemistry offers a

complementary framework that enables molecular-level

insights into structure–activity relationships and reaction

mechanisms.

Density Functional Theory (DFT) has emerged as a

widely adopted quantum mechanical method for

investigating the electronic structure and antioxidant

mechanisms of phenolic compounds. It allows precise

calculation of thermodynamic parameters such as bond

dissociation enthalpy (BDE), ionization potential (IP),

and proton affinity (PA), which are directly linked to

antioxidant efficiency (Silva et al., 2009).

In parallel, in silico techniques such as molecular docking

and molecular dynamics simulations facilitate the

prediction of ligand–protein interactions, enabling the

identification of potential therapeutic targets. For

instance, docking studies have demonstrated the binding

potential of plant-derived compounds to enzymes

involved in oxidative stress and inflammation, such as

cyclooxygenase (COX) and lipoxygenase (LOX)

(Chibuye et al., 2024).

Moreover, ADMET profiling tools have enhanced early-

stage drug discovery by predicting pharmacokinetic and

toxicity properties, reducing reliance on experimental

screening (Aslan et al., 2023).

1.3 Scope and Objectives

This review aims to provide a systematic and critically

evaluated synthesis of the application of DFT and in silico

methodologies in the study of Lamiaceae phytochemicals.

Specifically, it seeks to:

 Elucidate the quantum chemical basis of

antioxidant mechanisms

 Analyze DFT-derived descriptors relevant to

radical scavenging

 Examine molecular docking and multi-target

interactions

 Evaluate QSAR and ADMET integration in drug

discovery

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3 Journal of Research in Biology (2025) 15(4): 1-15

 Identify methodological limitations and research

gaps

By integrating computational and phytochemical

perspectives, this review contributes to a more robust

understanding of Lamiaceae-derived compounds as

candidates for therapeutic development.

2. Methodology: Systematic Review Protocol

A systematic review methodology was adopted to

critically evaluate the application of Density Functional

Theory (DFT) and in silico approaches in the

investigation of Lamiaceae phytochemicals. The review

process was designed in accordance with the Preferred

Reporting Items for Systematic Reviews and Meta-

Analyses (PRISMA) guidelines to ensure transparency,

reproducibility, and methodological rigor. A

comprehensive literature search was conducted across

major scientific databases, including Scopus, Web of

Science, PubMed, and Google Scholar, to identify

relevant peer-reviewed studies.

The search strategy employed a combination of keywords

and Boolean operators, including “Lamiaceae,” “density

functional theory,” “DFT,” “in silico,” “molecular

docking,” “molecular dynamics,” “QSAR,” “ADMET,”

“antioxidant,” and “phytochemicals,” along with specific

compound names such as “rosmarinic acid,” “luteolin,”

“thymol,” “carvacrol,” “carnosic acid,” and “ursolic

acid.” The search was restricted to articles published in

English, with no lower date limitation imposed in order to

capture both foundational and recent developments in

computational phytochemistry. Studies published up to

2024 were considered for inclusion.

Eligibility criteria were established to ensure the

relevance and quality of the selected studies. Articles were

included if they employed DFT calculations and/or in

silico methodologies, including molecular docking,

molecular dynamics simulations, QSAR modeling, or

ADMET profiling, and focused specifically on

phytochemicals derived from the Lamiaceae family.

Additionally, only studies reporting quantitative

computational descriptors, such as bond dissociation

enthalpy (BDE), ionization potential (IP), proton affinity

(PA), HOMO–LUMO energy gaps, or binding affinity

values, were considered. Studies were excluded if they

lacked computational analysis, were not directly related to

Lamiaceae phytochemicals, or were published as

conference abstracts, editorials, or non-peer-reviewed

reports.

Data extraction was performed using a structured

framework to ensure consistency across studies. Extracted

information included the identity of the phytochemical,

the computational methods employed, key quantum

chemical descriptors, biological targets, and principal

findings. Particular attention was given to the level of

theory used in DFT calculations, the choice of basis sets,

and the inclusion of solvent models such as the

Polarizable Continuum Model (PCM) or Solvation Model

Density (SMD). The methodological quality of the

included studies was further evaluated based on the clarity

of computational protocols and, where available,

validation against experimental data. Studies that did not

provide sufficient methodological detail or

reproducibility were excluded from the final synthesis.

3. Theoretical Framework: Density Functional Theory

and In Silico Methodologies

Density Functional Theory (DFT) has emerged as a

cornerstone computational approach in the investigation

of phytochemical systems, particularly due to its ability to

provide accurate electronic structure information at a

relatively moderate computational cost. Unlike

wavefunction-based methods, DFT describes molecular

systems in terms of electron density, thereby simplifying

the treatment of many-electron interactions. The

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theoretical foundation of DFT is established by the

Hohenberg–Kohn theorems, which state that the ground-

state properties of a many-electron system are uniquely

determined by its electron density and that the correct

electron density minimizes the total energy of the system.

The Kohn–Sham formulation further facilitates practical

implementation by transforming the many-body problem

into a set of one-electron equations.

In the context of phytochemical research, DFT is

extensively employed to elucidate antioxidant

mechanisms at the molecular level. Hybrid functionals

such as B3LYP, in combination with basis sets like 6-

31G(d,p) or 6-311+G(d,p), are widely used due to their

demonstrated reliability in predicting thermodynamic

properties of phenolic compounds (Silva et al., 2009).

These calculations enable the determination of key

descriptors that govern antioxidant activity, including

bond dissociation enthalpy (BDE), ionization potential

(IP), proton affinity (PA), and electron transfer enthalpy

(ETE).

The antioxidant behavior of phenolic phytochemicals is

generally explained through three principal mechanisms:

hydrogen atom transfer (HAT), single electron transfer–

proton transfer (SET–PT), and sequential proton loss–

electron transfer (SPLET). In the HAT mechanism, the

antioxidant donates a hydrogen atom to neutralize free

radicals, and the efficiency of this process is primarily

determined by the bond dissociation enthalpy of the O–H

bond. Lower BDE values indicate a greater propensity for

hydrogen donation and, consequently, higher antioxidant

activity. In contrast, the SET–PT mechanism involves an

initial electron transfer step followed by proton

dissociation, with ionization potential and proton

dissociation enthalpy serving as the governing

parameters. The SPLET mechanism, which is particularly

relevant in polar environments such as aqueous biological

systems, involves initial proton loss followed by electron

transfer, and is characterized by proton affinity and

electron transfer enthalpy.

In addition to thermodynamic descriptors, Frontier

Molecular Orbital (FMO) theory provides critical insights

into the reactivity of phytochemicals. The energies of the

highest occupied molecular orbital (HOMO) and lowest

unoccupied molecular orbital (LUMO) are directly related

to the electron-donating and electron-accepting

capabilities of a molecule, respectively. The energy gap

between these orbitals serves as an indicator of chemical

reactivity, with smaller gaps corresponding to higher

reactivity and enhanced antioxidant potential.

Complementary to DFT calculations, molecular docking

has become an indispensable tool for predicting the

interaction of phytochemicals with biological targets.

Docking algorithms estimate binding affinities and

identify key interactions, such as hydrogen bonding and

hydrophobic contacts, within the active sites of proteins.

For instance, computational studies have demonstrated

that plant-derived compounds can exhibit significant

binding affinity toward inflammatory targets such as

cyclooxygenase-2 (COX-2), highlighting their

therapeutic potential (Chibuye et al., 2024).

Molecular dynamics (MD) simulations further extend

these insights by capturing the dynamic behavior of

ligand–protein complexes over time. By analyzing

parameters such as root mean square deviation (RMSD)

and binding free energy, MD simulations provide a more

comprehensive understanding of the stability and

conformational flexibility of these complexes under

physiological conditions. These simulations are

particularly valuable in validating docking results and

refining predictions of binding interactions.

Quantitative Structure–Activity Relationship (QSAR)

modeling integrates DFT-derived descriptors with

statistical approaches to establish predictive relationships

between molecular structure and biological activity. Such

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models enable the virtual screening of large compound

libraries and facilitate the identification of promising

candidates for further investigation. Additionally,

ADMET profiling plays a critical role in evaluating the

pharmacokinetic and toxicological properties of

phytochemicals, allowing early-stage assessment of drug-

likeness and safety. Computational tools used in ADMET

prediction provide valuable insights into parameters such

as absorption, distribution, metabolism, excretion, and

toxicity, thereby supporting the rational design of

therapeutically viable compounds (Aslan et al., 2023).

4. Chemical Diversity and Structural Classification of

Lamiaceae Phytochemicals

The Lamiaceae family is widely recognized for its

extensive repertoire of bioactive secondary metabolites,

particularly phenolic acids, flavonoids, and terpenoids,

which collectively underpin its pharmacological

significance. These compounds differ in their structural

frameworks, electronic configurations, and functional

group distributions, all of which influence their

antioxidant behavior and biological activity. The chemical

diversity of Lamiaceae has been extensively

characterized, with numerous studies highlighting the

dominance of polyphenolic constituents as primary

contributors to radical scavenging activity (Damašius et

al., 2014; Buathong & Duangsrisai, 2023; Aryal et al.,

2024). The major phytochemical classes present in

Lamiaceae species and their associated biological

activities are summarized in Table 1 (Mucha et al., 2021;

Aryal et al., 2024).

Table 1. Major phytochemical classes of Lamiaceae species with representative compounds and reported

biological activities.

Phytochemical Class Representative

Compounds

Major Sources

(Lamiaceae)

Key Biological Activities

Phenolic acids Rosmarinic acid,

Caffeic acid

Rosmarinus officinalis,

Salvia officinalis

Antioxidant, anti-inflammatory,

antimicrobial

Flavonoids Luteolin, Quercetin,

Apigenin

Mentha spicata, Ocimum

basilicum

Antioxidant, antiviral,

cardioprotective

Terpenoids

(monoterpenes)

Thymol, Carvacrol Thymus vulgaris, Origanum

vulgare

Antimicrobial, anti-

inflammatory

Terpenoids

(diterpenes)

Carnosic acid,

Carnosol

Rosmarinus officinalis,

Salvia officinalis

Strong antioxidant,

neuroprotective

Other phenolics Eugenol Ocimum tenuiflorum Antioxidant, antimicrobial

4.1 Phenolic Acids

Phenolic acids are among the most abundant and

biologically active constituents of Lamiaceae species,

with rosmarinic acid representing a hallmark compound

of this family. Structurally, rosmarinic acid contains two

catechol moieties, which significantly enhance its

antioxidant capacity through efficient hydrogen atom

donation and resonance stabilization of phenoxyl radicals.

The presence of ortho-dihydroxy groups is particularly

important, as these configurations facilitate

intramolecular hydrogen bonding and electron

delocalization, thereby lowering the bond dissociation

enthalpy of hydroxyl groups and promoting radical

scavenging activity (Damašius et al., 2014; Aryal et al.,

2024).

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In addition to rosmarinic acid, other phenolic acids such

as caffeic acid and related hydroxycinnamic derivatives

contribute significantly to the antioxidant profile of

Lamiaceae plants. These compounds exhibit conjugated

π-electron systems that enable effective stabilization of

radical intermediates, a feature that has been consistently

correlated with enhanced antioxidant performance.

Experimental studies employing HPLC-DPPH assays

have demonstrated strong radical scavenging activity in

extracts rich in these compounds, particularly in species

such as rosemary, sage, and oregano (Damašius et al.,

2014). Furthermore, computational analyses have

supported these observations by linking reduced bond

dissociation enthalpy values with increased antioxidant

efficiency (Silva et al., 2009).

4.2 Flavonoids

Flavonoids constitute another major class of Lamiaceae

phytochemicals, characterized by a C₆–C₃–C₆ backbone

consisting of two aromatic rings connected by a

heterocyclic ring. Variations in hydroxylation patterns and

conjugation significantly influence their antioxidant

activity. Among these compounds, luteolin and quercetin

are particularly notable due to the presence of a catechol

moiety in the B-ring, which serves as the primary site for

radical scavenging.

The antioxidant activity of flavonoids is strongly

influenced by three key structural features: (i) the

presence of ortho-dihydroxy groups in the B-ring, (ii)

conjugation between the B-ring and the C-ring, and (iii)

the presence of a 2,3-double bond in conjunction with a

4-oxo function. These features collectively enhance

electron delocalization and stabilize radical intermediates,

thereby facilitating both hydrogen atom transfer and

electron transfer mechanisms. Comparative studies have

shown that flavonoids possessing these structural

elements exhibit significantly higher antioxidant activity

than those lacking them, as exemplified by the lower

activity of apigenin relative to luteolin (Aryal et al., 2024).

In addition to their antioxidant properties, flavonoids

exhibit a wide range of biological activities, including

anti-inflammatory and antiviral effects. Their planar

structures enable π–π stacking interactions with aromatic

residues in protein binding sites, which is particularly

relevant in molecular docking studies. This structural

versatility supports their role as multi-target bioactive

compounds in computational drug discovery (Buathong &

Duangsrisai, 2023).

4.3 Terpenoids

Terpenoids represent a structurally diverse class of

phytochemicals within the Lamiaceae family,

encompassing monoterpenes, sesquiterpenes, diterpenes,

and triterpenes. While many terpenoids lack the extensive

conjugation and multiple hydroxyl groups characteristic

of phenolic compounds, certain subclasses particularly

diterpenes such as carnosic acid exhibit significant

antioxidant activity due to the presence of phenolic

functionalities.

Carnosic acid, a major constituent of rosemary and sage,

contains a catechol moiety that enables efficient radical

scavenging through hydrogen atom transfer mechanisms.

Upon oxidation, it can be converted into carnosol, which

retains antioxidant activity through its phenolic structure.

These compounds have been extensively studied for their

ability to inhibit lipid peroxidation and oxidative stress,

highlighting their therapeutic relevance (Damašius et al.,

2014).

Monoterpenes such as thymol and carvacrol, which are

abundant in Lamiaceae essential oils, exhibit moderate

antioxidant activity due to the presence of a single

phenolic hydroxyl group. Although their radical

scavenging capacity is lower than that of polyphenolic

compounds, their lipophilicity enhances membrane

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permeability and contributes to their antimicrobial and

anti-inflammatory effects. These properties underscore

the complementary role of terpenoids in the overall

bioactivity of Lamiaceae species (Buathong &

Duangsrisai, 2023).

5. DFT Insights into Antioxidant Mechanisms of

Lamiaceae Phytochemicals

Density Functional Theory has played a pivotal role in

elucidating the antioxidant mechanisms of Lamiaceae

phytochemicals by providing quantitative insights into

their thermodynamic and electronic properties. Through

the calculation of parameters such as bond dissociation

enthalpy, ionization potential, proton affinity, and electron

transfer enthalpy, DFT enables the prediction of radical

scavenging pathways and reactivity trends in phenolic

compounds.

One of the most important descriptors in antioxidant

studies is the bond dissociation enthalpy of the O–H bond,

which governs the efficiency of hydrogen atom transfer

mechanisms. Phenolic compounds with lower BDE

values exhibit greater antioxidant activity due to their

enhanced ability to donate hydrogen atoms and stabilize

the resulting radicals. Computational studies have

demonstrated that catechol-containing compounds, such

as rosmarinic acid and luteolin, possess significantly

lower BDE values compared to simple phenols, primarily

due to resonance stabilization and intramolecular

hydrogen bonding (Silva et al., 2009; Damašius et al.,

2014). The fundamental antioxidant mechanisms of

phenolic compounds, including hydrogen atom transfer

(HAT), single electron transfer–proton transfer (SET–

PT), and sequential proton loss–electron transfer

(SPLET), are illustrated in Figure 1 (Mucha et al., 2021;

Silva et al., 2009).

In addition to hydrogen atom transfer, electron transfer

mechanisms also contribute to antioxidant activity. The

ionization potential reflects the ease with which a

molecule can donate an electron, while proton affinity

determines its ability to participate in proton transfer

reactions. These parameters are particularly relevant in

polar environments, where the sequential proton loss–

electron transfer mechanism becomes thermodynamically

favorable. Such behavior is consistent with biological

systems, where solvent effects play a crucial role in

modulating antioxidant activity.

Frontier molecular orbital analysis further enhances the

understanding of antioxidant behavior by linking

electronic structure to reactivity. Molecules with higher

HOMO energies exhibit greater electron-donating

capacity, while smaller HOMO–LUMO gaps are

associated with increased chemical reactivity.

Polyphenolic compounds from Lamiaceae typically

display smaller energy gaps compared to monoterpenes,

which explains their superior antioxidant performance.

Moreover, the integration of DFT-derived descriptors with

experimental antioxidant assays has demonstrated strong

correlations between computational predictions and

biological activity. These findings support the use of DFT

as a reliable tool for screening and optimizing

phytochemicals for therapeutic applications. The ability to

predict antioxidant mechanisms at the molecular level

provides a valuable framework for the rational design of

bioactive compounds and the identification of promising

candidates for further investigation. Key DFT-derived

descriptors governing antioxidant activity of Lamiaceae

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phytochemicals are summarized in Table 2,

demonstrating their structure–activity relationships

(Merecz-Sadowska et al., 2023).

Table 2. Important DFT descriptors and their relevance to antioxidant mechanisms.

Descriptor Full Form Mechanistic Role Interpretation

BDE Bond Dissociation Enthalpy HAT mechanism Lower BDE → higher antioxidant

activity

IP Ionization Potential SET mechanism Lower IP → easier electron donation

PA Proton Affinity SPLET mechanism Lower PA → easier proton dissociation

ETE Electron Transfer Enthalpy SPLET mechanism Lower ETE → better electron transfer

HOMO Highest Occupied Molecular

Orbital

Electron donation Higher HOMO → stronger antioxidant

LUMO Lowest Unoccupied Molecular

Orbital

Electron acceptance Lower LUMO → higher reactivity

Figure 1. Proposed antioxidant mechanisms of phenolic compounds: (A) hydrogen atom transfer (HAT),

(B) single electron transfer–proton transfer (SET–PT), and (C) sequential proton loss–electron transfer

(SPLET).

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6. Molecular Docking and Multi-Target Therapeutic

Potential of Lamiaceae Phytochemicals

The integration of molecular docking with Density

Functional Theory has significantly advanced the

identification of Lamiaceae phytochemicals as multi-

target therapeutic agents. Molecular docking enables the

prediction of binding affinity and interaction profiles

between phytochemicals and biologically relevant targets,

while DFT provides complementary insights into the

electronic properties governing ligand reactivity.

Together, these approaches form a robust computational

framework for rational drug discovery.

Recent computational studies have demonstrated that

phytochemicals derived from medicinal plants exhibit

strong binding affinities toward key inflammatory

enzymes, particularly cyclooxygenase-2 (COX-2) and

lipoxygenase (LOX), which play central roles in the

biosynthesis of pro-inflammatory mediators. For instance,

docking-based investigations have reported binding

energies in the range of −8.0 to −10.0 kcal/mol for

phenolic compounds interacting with COX-2, indicating

significant inhibitory potential (Rudrapal et al., 2025).

These findings are further supported by studies

demonstrating dual inhibition of COX and LOX pathways

by plant-derived compounds, suggesting a synergistic

mechanism for anti-inflammatory activity (Rudrapal et

al., 2023).

Flavonoids such as luteolin and quercetin have been

widely investigated for their interaction with COX-2 due

to their planar structures and ability to form hydrogen

bonds and π–π interactions within enzyme active sites.

Molecular docking and DFT analyses have revealed that

these compounds exhibit stable binding conformations,

supported by favorable electronic descriptors such as high

HOMO energy and low energy gaps, which facilitate

electron donation and interaction with catalytic residues

(Lakkadi et al., 2024; Nadia et al., 2024). These

interactions are often stabilized by hydrogen bonding with

key amino acid residues and hydrophobic interactions

within the binding pocket, contributing to their inhibitory

activity.

In addition to anti-inflammatory targets, Lamiaceae

phytochemicals have demonstrated potential against viral

and metabolic targets. Computational studies have

identified several plant-derived compounds as inhibitors

of viral proteases, including SARS-CoV-2 main protease

(Mpro), with binding affinities comparable to reference

antiviral drugs. These findings highlight the potential of

phytochemicals as broad-spectrum therapeutic agents

(Acosta-Quiroga et al., 2025). The binding interactions of

flavonoids with inflammatory targets such as COX-2 are

exemplified in Figure 3, highlighting key hydrogen

bonding and hydrophobic interactions within the active

site (Rudrapal et al., 2023).

Figure 3. Representative binding interactions of luteolin with COX-2 (PDB ID: 5IKR), showing key hydrogen

bonding and hydrophobic contacts.

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10 Journal of Research in Biology (2025) 15(4): 1-15

Furthermore, molecular docking studies have shown that

phenolic compounds can interact with metabolic enzymes

such as α-amylase and α-glucosidase, suggesting potential

applications in the management of metabolic disorders

such as diabetes. The ability of these compounds to bind

multiple targets underscores their relevance in

polypharmacology, where a single molecule can modulate

multiple biological pathways simultaneously.

Molecular dynamics simulations further validate docking

results by providing insights into the stability and

conformational behavior of ligand–protein complexes

over time. Studies employing MD simulations have

demonstrated that phytochemical–protein complexes

exhibit stable trajectories, low root mean square deviation

(RMSD) values, and favorable binding free energies,

confirming the reliability of docking predictions

(Alhumaydhi et al., 2021). These dynamic analyses are

essential for understanding the behavior of complexes

under physiological conditions and for refining drug

candidates.

The integration of docking, DFT, and ADMET profiling

has enabled the identification of phytochemicals with

favorable pharmacokinetic properties, including adequate

bioavailability, low toxicity, and optimal lipophilicity.

Such multi-parameter optimization is crucial in early-

stage drug discovery, as it reduces the likelihood of failure

in later stages of development. Recent studies have

emphasized the importance of combining computational

methods to achieve a comprehensive evaluation of

bioactive compounds, thereby accelerating the discovery

pipeline (Babalola et al., 2025). Representative molecular

docking interactions of selected Lamiaceae

phytochemicals with key therapeutic targets are presented

in Table 3, highlighting their multi-target potential

(Rudrapal et al., 2023; Parvin et al., 2025).

Table 3. Molecular docking results of selected Lamiaceae phytochemicals against key biological targets.

Compound Target

Protein

Binding Affinity

(kcal/mol)

Interaction

Type

Therapeutic

Area

Key

Residues/Mechanism

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11 Journal of Research in Biology (2025) 15(4): 1-15

Rosmarinic

acid

COX-2 ~ -9.2 H-bond +

hydrophobic

Anti-

inflammatory

Active site hydrogen

bonding

Rosmarinic

acid

SARS-CoV-

2 Mpro

~ -8.5 H-bonding Antiviral Protease inhibition

Luteolin COX-2 ~ -9.5 π-π stacking +

H-bond

Anti-

inflammatory

Aromatic interactions

Luteolin PLpro ~ -8.9 Hydrogen

bonding

Antiviral Protease inhibition

Carnosic acid COX-2 ~ -9.8 Hydrophobic +

H-bond

Anti-

inflammatory

Strong active site

binding

Ursolic acid COX-2 ~ -10.2 Hydrophobic Anti-

inflammatory

High binding affinity

Thymol Bacterial

proteins

-5.0 to -7.5 Hydrophobic Antimicrobial Membrane disruption

Carvacrol CYP51 ~ -8.2 Hydrophobic Antifungal Enzyme inhibition

Kaempferol α-amylase ~ -9.5 H-bond Antidiabetic Enzyme inhibition

Overall, the convergence of molecular docking and

quantum chemical approaches has established Lamiaceae

phytochemicals as promising candidates for multi-target

therapeutic applications. Their ability to interact with

diverse biological targets, combined with favorable

electronic and pharmacokinetic properties, highlights

their potential in the development of novel antioxidant,

anti-inflammatory, antiviral, and metabolic therapeutics.

7. QSAR Modeling and ADMET Profiling in

Lamiaceae-Based Drug Discovery

Quantitative Structure–Activity Relationship (QSAR)

modeling has emerged as a powerful computational tool

for establishing predictive relationships between

molecular structure and biological activity. In the context

of Lamiaceae phytochemicals, QSAR models have been

widely employed to correlate Density Functional Theory

(DFT)-derived descriptors with antioxidant and anti-

inflammatory activities. Parameters such as bond

dissociation enthalpy, ionization potential, proton affinity,

HOMO–LUMO energy gap, dipole moment, and

molecular volume have been shown to significantly

influence radical scavenging efficiency and enzyme

inhibition potential.

Recent studies have demonstrated that the integration of

quantum chemical descriptors into QSAR frameworks

enhances predictive accuracy, particularly for phenolic

compounds. For instance, polyphenolic structures

exhibiting lower bond dissociation enthalpy and higher

HOMO energy values tend to display superior antioxidant

activity, consistent with their enhanced electron- and

hydrogen-donating capacity (Merecz-Sadowska et al.,

2023). Moreover, machine learning-assisted QSAR

models have further improved predictive capabilities by

incorporating large descriptor datasets and nonlinear

relationships, thereby enabling high-throughput virtual

screening of phytochemicals (Babalola et al., 2025).

In parallel, ADMET (Absorption, Distribution,

Metabolism, Excretion, and Toxicity) profiling has

become an essential component of computational drug

discovery pipelines. Early-stage evaluation of

Kores et al., 2025

12 Journal of Research in Biology (2025) 15(4): 1-15

pharmacokinetic and toxicological properties is critical

for identifying compounds with favorable drug-like

characteristics. Computational tools have been widely

applied to predict parameters such as intestinal

absorption, blood–brain barrier permeability, cytochrome

P450 interactions, and toxicity profiles. Studies have

shown that many Lamiaceae-derived phytochemicals

exhibit favorable ADMET properties, including moderate

lipophilicity, acceptable bioavailability, and low predicted

toxicity, supporting their potential as therapeutic agents

(Parvin et al., 2025; Rudrapal et al., 2025).

The integration of QSAR and ADMET analyses with

molecular docking and DFT calculations provides a

comprehensive framework for rational drug design. This

multi-parameter approach allows for the simultaneous

optimization of biological activity and pharmacokinetic

properties, thereby reducing the likelihood of late-stage

failure in drug development. Such integrative strategies

are increasingly recognized as essential for the efficient

identification of lead compounds from natural product

libraries.

8. Integrated Computational Pipeline for Lamiaceae

Phytochemicals

The convergence of DFT, molecular docking, molecular

dynamics simulations, QSAR modeling, and ADMET

profiling has established a robust computational pipeline

for the systematic evaluation of phytochemicals. This

integrated approach enables the identification of

promising bioactive compounds through a stepwise

process that combines electronic structure analysis with

biological target prediction and pharmacokinetic

assessment.

Typically, DFT calculations are first employed to evaluate

the intrinsic reactivity of phytochemicals by determining

thermodynamic and electronic descriptors. These

descriptors are subsequently incorporated into QSAR

models to predict biological activity and prioritize

compounds for further investigation. Molecular docking

is then used to assess binding affinity and interaction

profiles with specific biological targets, while molecular

dynamics simulations provide insights into the stability

and conformational behavior of ligand–protein complexes

under dynamic conditions.

Finally, ADMET profiling is conducted to evaluate drug-

likeness and safety, ensuring that selected compounds

possess favorable pharmacokinetic properties. This

integrated workflow has been successfully applied in

numerous studies to identify phytochemicals with

potential therapeutic applications, particularly in the

context of inflammation, oxidative stress, and metabolic

disorders (Acosta-Quiroga et al., 2025). Table 4

summarises the key ADMET characteristics for

representative phytochemicals from the Lamiaceae

family.

Table 4: ADMET Parameters of Representative Lamiaceae Phytochemicals

Compound MW (Da) LogP HBD HBA TPSA (Å²) LogS BBB Toxicity

Rosmarinic acid 360.3 1.5 4 8 138 -2.5 Low Low

Luteolin 286.2 2.1 4 6 111 -3.2 Moderate Low

Apigenin 270.2 2.5 3 5 91 -3.5 Moderate Low

Quercetin 302.2 1.5 5 7 131 -2.8 Low Low

Thymol 150.2 3.3 1 1 20 -2.1 High Low

Carvacrol 150.2 3.5 1 1 20 -2.2 High Low

Carnosic acid 332.4 3.5 2 4 77 -4.2 Moderate Low

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13 Journal of Research in Biology (2025) 15(4): 1-15

Ursolic acid 456.7 5.5 2 3 57 -6.2 Low Low

The adoption of such multi-disciplinary computational

strategies represents a significant advancement in natural

product research, as it enables the efficient screening and

optimization of complex phytochemical libraries.

Moreover, the integration of machine learning and

artificial intelligence with traditional computational

methods is expected to further enhance predictive

accuracy and accelerate drug discovery processes. The

overall computational strategy integrating DFT,

molecular docking, molecular dynamics simulations,

QSAR modeling, and ADMET profiling is summarized in

Figure 2 (Babalola et al., 2025; Alhumaydhi et al., 2021).

9. Conclusion and Future Perspectives

The present review highlights the critical role of Density

Functional Theory and in silico methodologies in

advancing the understanding of Lamiaceae

phytochemicals and their therapeutic potential. Through

the integration of quantum chemical calculations,

molecular docking, molecular dynamics simulations,

QSAR modeling, and ADMET profiling, significant

progress has been made in elucidating the mechanisms

underlying antioxidant and pharmacological activities.

Phenolic acids and flavonoids have emerged as the most

potent antioxidant constituents of Lamiaceae, primarily

due to their favorable electronic properties and structural

features, including catechol moieties and extended

conjugation systems. Terpenoids, although generally less

active as antioxidants, contribute to the overall

pharmacological profile through complementary

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14 Journal of Research in Biology (2025) 15(4): 1-15

mechanisms, including anti-inflammatory and

antimicrobial effects. DFT studies have provided valuable

insights into the thermodynamic and electronic factors

governing these activities, while docking and molecular

dynamics simulations have elucidated their interactions

with key biological targets.

Despite these advancements, several challenges remain.

The accurate modeling of solvent effects and biological

environments continues to be a limitation in DFT studies,

while the reliability of docking predictions is often

dependent on the quality of protein structures and scoring

functions. Additionally, the complexity of biological

systems necessitates the development of more

sophisticated multi-target and systems-level approaches.

Future research should focus on the integration of

machine learning with quantum chemical descriptors to

enhance predictive capabilities and enable the discovery

of novel bioactive compounds. Furthermore, the

validation of computational predictions through

experimental studies remains essential for translating in

silico findings into practical therapeutic applications. The

continued development of hybrid computational–

experimental frameworks will be critical for unlocking

the full potential of Lamiaceae phytochemicals in drug

discovery.

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