Sciencelet Molecular DiscoveryOriginal research

Vol. 1
No. 1
ISSN: (in Review)

In silico molecular docking and ADMET profiling of the phytochemicals from Abies alba against mpox virus

1,2 (corresponding author)ORCID iD ORCID profile,
3ORCID iD ORCID profile
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  1. 1.School of Pharmacy, BRAC University, Dhaka 1212, Bangladesh
  2. 2.Department of Pharmacy, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
  3. 3.Department of Microbiology, University of Rajshahi, Rajshahi 6205, Bangladesh
Corresponding author
Published: Jul 11, 2026
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Keywords: mpox, Abies alba, docking, ADMET

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Abstract

Mpox virus (MPXV) is a zoonotic, double-stranded DNA virus from the Orthopoxvirus genus. It has become a concern due to its recent outbreak. To date, there are no antiviral drugs specifically designed and officially approved by the Food and Drug Administration (FDA) for mpox. This study aims to investigate the phytochemicals from the Abies alba plant to find its antiviral properties against MPXV. Molecular docking and ADMET profiling were performed in this study to find the best-scoring compounds. The DNA polymerase enzyme and the E8L cell surface-binding protein of MPXV were involved as the targets in molecular docking. The E8L cell surface-binding protein was modelled since there was no deposited structure available. A total of 82 phytochemicals from A. alba and two standard compounds—tecovirimat and trifluridine—were subjected to site-specific molecular docking. Against the DNA polymerase enzyme, neoabietic acid and palustric acid showed the lowest binding affinities (−9.70 and −9.34 kcal/mol, respectively), which surpass those of tecovirimat (−8.54 kcal/mol) and trifluridine (−7.31 kcal/mol). In contrast, isocembrene and neoabietic acid showed the lowest and the nearest binding affinities (−7.20 kcal/mol for both) against the E8L cell surface-binding protein compared to that of tecovirimat (−7.59 kcal/mol) and trifluridine (−5.90 kcal/mol). Adequate protein-ligand interactions were observed, which involved hydrogen bonds, van der Waals forces, and hydrophobic interactions. Moreover, the ADMET profiles of these compounds were also found acceptable. These findings indicate the promising activities of the phytochemicals from A. alba, which may play a significant role in future drug development against the mpox virus. However, this study relies on computational analyses only. Further in silico analysis through molecular dynamics, followed by in vitro and in vivo investigations, is recommended for establishing the potential of these compounds.

Introduction

Mpox, caused by the double-stranded DNA virus MPXV (genus Orthopoxvirus, family Poxviridae), has emerged as a significant public health concern beyond its historical endemic borders in Central and West Africa [1]. Recent multi-country outbreaks marked by sustained human-to-human transmission—occurring via close physical contact, respiratory droplets, and contaminated materials—demonstrate a dramatic shift in the virus's global footprint [2]. The rapid spread of distinct genetic lineages, particularly Clade I variants and Clade IIb, prompted the World Health Organization to designate MPXV as a Public Health Emergency of International Concern [3]. Patients typically present with systemic symptoms such as fever, painful lymphadenopathy, and characteristic maculopapular or vesicular skin lesions [4]. Severe cases can lead to secondary bacterial infections, encephalitis, or death, with heightened vulnerability observed in immunocompromised patients and young children [5]. Because current clinical options are mostly limited to supportive care and repurposed drugs, finding target-specific antiviral therapies remains an urgent clinical goal.

The replication mechanism of MPXV depends on several conserved proteins that serve as prime candidate targets for small-molecule inhibition [6]. Critical among these are the F8L subunit of the DNA polymerase complex, responsible for viral genome replication; the VP39 methyltransferase, which mediates mRNA cap modification; and the F13L/VP37 envelope protein, essential for viral egress and extracellular dissemination [7]. At present, clinical interventions rely heavily on off-label or emergency-use therapeutics developed originally for smallpox, including tecovirimat (TPOXX), cidofovir, and brincidofovir [8]. However, these options face major clinical boundaries. Tecovirimat targets VP37, but single amino acid substitutions in the F13L gene can quickly produce high-level resistance, threatening its utility during extended treatment [8]. Meanwhile, drugs like cidofovir and brincidofovir are hampered by substantial dose-dependent nephrotoxicity, poor oral availability, and toxicity to different organs [9, 10]. These limitations emphasize the need to discover new chemical leads that inhibit key viral targets with higher resistance barriers and better safety profiles.

Plant secondary metabolites provide a rich starting point for drug discovery, offering chemical diversity that synthetic libraries often lack [11]. Natural compounds frequently possess multitarget mechanisms of action, allowing them to disturb multiple steps in viral maturation or host-pathogen interactions simultaneously [12]. This polypharmacological capacity can reduce the likelihood that a virus will rapidly evolve escape mutations, a frequent failure mode for single-target synthetic agents [13]. Furthermore, many plant-derived compounds, such as flavonoids, polyphenols, and terpenes, carry inherent anti-inflammatory and antioxidant activities that may lessen tissue damage caused by host inflammatory responses during viral infection [1417]. Evaluating specialized plant extracts thus offers a pragmatic pathway to identify structurally distinct scaffolds with therapeutic potential against emerging viral pathogens like MPXV.

Abies alba Mill., commonly known as the European silver fir, is a widespread conifer within the Pinaceae family with an established record of use in traditional European medicine for treating respiratory ailments and skin inflammation [18]. Chemical analyses of A. alba bark, needles, and essential oils show a rich composition of secondary metabolites, including monoterpenes (alpha-pinene, beta-pinene, limonene), diterpenes such as abietic and dehydroabietic acids, lignans, and bioactive flavonoids including quercetin derivatives and more [18]. Experimental studies have confirmed that A. alba extracts possess notable antioxidant, anti-inflammatory, and antimicrobial activities [1921]. Despite this established bioactive profile, the potential of A. alba constituents to inhibit specific MPXV enzymatic targets has not yet been investigated, leaving a promising pool of natural compounds unexamined.

Computer-aided drug design (CADD) provides an efficient, systematic framework for initial compound screening and lead optimization [22]. Structure-based molecular docking allows researchers to evaluate how small molecules interact with target binding pockets, predicting binding geometries, relative binding free energies, and specific non-covalent interactions such as hydrogen bonds and hydrophobic contacts [23]. When combined with early in silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling and drug-likeness rules (such as Lipinski's rule of five), computational approaches can quickly filter out compounds with unfavorable oral absorption, metabolic instability, or predicted toxicity [24]. Integrating binding affinity predictions with pharmacokinetic screening helps prioritize top-performing candidates before moving to resource-intensive wet-lab assays.

Although A. alba contains a diverse set of bioactive phytochemicals, its therapeutic potential against MPXV target proteins remains unexplored. To address this gap, this study uses a comprehensive in silico workflow involving molecular docking and ADMET characterization to systematically screen A. alba phytochemicals against critical MPXV functional targets, specifically the DNA polymerase subunit F8L and the egress protein F13L. By comparing the binding dynamics and key residue interactions of these natural molecules against benchmark antivirals like tecovirimat, this work seeks to identify non-toxic, high-affinity lead candidates with favorable pharmacokinetic properties. Ultimately, the insights gained here establish a theoretical and structural foundation to inform future experimental validation and formulation of natural product-based anti-MPXV therapeutics.

Materials and Methods

Ligand Structures Retrieval and Preparation

The structures of the compounds in 3D SDF formats were downloaded from the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database (https://cb.imsc.res.in/imppat/) [25, 26]. A total of 82 compounds were enlisted in the IMPPAT database for the Abies alba plant across different parts. Tecovirimat and trifluridine were selected as standard drugs, and their structures in 3D SDF formats were downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/) [27]. These were taken as standard drugs for their proven antiviral activities against mpox and other viruses [28, 29]. To meet physiological pH and charge conditions, all the ligand structures were protonated at pH 7.4, and Gasteiger charges were added using Open Babel 3.1.0 software [30]. All ligands were saved into PDBQT format, which is the required input format for the AutoDock Vina program.

Protein Structures Retrieval and Modeling

The DNA polymerase enzyme and the E8L cell surface-binding protein of MPXV were chosen as targets in this study. The 3D structure of DNA polymerase (PDB ID: 8K8U; 3.05 Å resolution) was downloaded in PDB format from the RCSB Protein Data Bank (https://www.rcsb.org/) [31, 32]. However, the structure of the E8L cell surface-binding protein was not available in the RCSB database. The FASTA sequence of the E8L cell surface-binding protein was downloaded from UniProtKB (Accession: Q8V4Y0; https://www.uniprot.org/uniprotkb) [33]. The 3D structure of the protein was modelled from the FASTA sequence using the AlphaFold 3 server (https://alphafoldserver.com/) of Google DeepMind [34, 35]. The structure of the predicted model was analyzed using the SAVES v6.1 - Structure Validation Server (UCLA-DOE LAB; https://saves.mbi.ucla.edu/). The Ramachandran plots were generated using the PROCHECK tool of the SAVES server [36].

Protein Structures Preparation for Docking

The binding site of the DNA polymerase enzyme was determined based on the co-crystallized ligand using the PyMOL Molecular Graphics System version 3.0 (Schrödinger, LLC) software, and that of the E8L cell surface-binding protein was predicted using the PrankWeb 4 (https://prankweb.cz/) [37, 38]. The downloaded structure of DNA polymerase was cleaned using PyMOL, and only the required chain (A) was kept for molecular docking. Afterwards, missing atoms were fixed, and polar hydrogens were added to the structures using AutoDock Tools 1.5.7 [39]. Consequently, Kollman charges were added to the structures, and nonpolar hydrogens were merged using the same software. The structures were then exported into PDBQT format [40].

Molecular Docking

The ligands were docked with the targets using the AutoDock Vina program [41]. While docking, the exhaustiveness was set to 16, nine docking poses were taken as output for each ligand, and the best poses were chosen as the final docking results. The binding affinities of the ligands were compared with those of the standard drugs, and the top ligands were shortlisted. The protein-ligand interactions were analyzed in detail using the BIOVIA Discovery Studio 2025 Client software (by Dassault Systèmes). The 3D and 2D diagrams were saved from the software in high resolution.

ADMET Profiling

The ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties were profiled incorporating the SwissADME (https://www.swissadme.ch/), Deep-PK (), and ProTox 3.0 (https://tox.charite.de/protox3/) tools [4244]. In ADMET, the molecular weight, topological polar surface area (TPSA), lipophilicity (logP), water solubility (logS), blood-brain barrier (BBB) permeability, metabolic enzyme inhibitions, gastrointestinal (GI) tract permeability, bioavailability, synthetic accessibility, Lipinski’s rule of five, Ghose filter, metabolism, half-life etc., were analyzed. From ProTox 3.0, the lethal dose 50 (LD50), toxicity class, and organ-level toxicity data were analyzed. All the properties of the test ligands were compared with the properties of the standard drugs.

Results

Analysis of the Modeled Protein

The 3D structure of the E8L cell surface-binding protein modeled in AlphaFold had a pTM score of 0.78 (Figure 1). Because this score is higher than 0.5, the predicted shape of the complex is likely close to the real structure [45]. The Ramachandran plot of the modeled protein shows that 253 (91.0 %) residues were in the most favored region. Moreover, 24 (8.6 %) residues were observed in the additional allowed region, and only the residue Val246 was in the generously allowed region, occupying the rest 0.4 %. The binding site of the modeled E8L has also been depicted in Figure 1(D).

Figure 1. (A) 3D modeled structure of the E8L cell surface-binding protein from the AlphaFold server; (B) Predicted aligned error (PAE) heatmap; (C) Ramachandran plot of the modeled structure; (D) Binding site of the modeled E8L obtained from PrankWeb.

Figure 1. (A) 3D modeled structure of the E8L cell surface-binding protein from the AlphaFold server; (B) Predicted aligned error (PAE) heatmap; (C) Ramachandran plot of the modeled structure; (D) Binding site of the modeled E8L obtained from PrankWeb.

Molecular Docking Analysis

The atomic-level interaction mechanisms governing the inhibition of DNA polymerase, molecular docking simulations were conducted for the abietane diterpenoids palustric acid and neoabietic acid, alongside the reference antiviral drug tecovirimat (Table 1 and Figure 2). Structural analysis reveals that both natural compounds effectively lodge within the catalytic channel of DNA polymerase, exploiting a conserved polar anchor in conjunction with extensive hydrophobic packing to stabilize their bound-state conformations.

Figure 2. Intermolecular interactions in the molecular docking complexes of (A) palustric acid-DNA polymerase in 3D, (B) palustric acid-DNA polymerase in 2D, (C) neoabietic acid-DNA polymerase in 3D, (D) neoabietic acid-DNA polymerase in 2D, (E) tecovirimat-DNA polymerase in 3D, and (F) tecovirimat-DNA polymerase in 2D.

Figure 2. Intermolecular interactions in the molecular docking complexes of (A) palustric acid-DNA polymerase in 3D, (B) palustric acid-DNA polymerase in 2D, (C) neoabietic acid-DNA polymerase in 3D, (D) neoabietic acid-DNA polymerase in 2D, (E) tecovirimat-DNA polymerase in 3D, and (F) tecovirimat-DNA polymerase in 2D.

Table 1. Molecular docking non-bonded interactions against DNA polymerase obtained from BIOVIA Discovery Studio Client 2025 software.

Compound Name Residue Distance (Å) Interaction Type
Palustric acid PHE494 2.46129 Conventional Hydrogen Bond
THR659 3.82478 Pi-Donor Hydrogen Bond
THR659 3.73991 Pi-Sigma
PHE494 5.09602 Pi-Pi T-shaped
ALA481 4.43084 Alkyl
LYS478 4.68323 Pi-Alkyl
PHE334 5.12056 Pi-Alkyl
PHE494 4.84629 Pi-Alkyl
PHE494 4.98030 Pi-Alkyl
Neoabietic acid PHE494 2.22483 Conventional Hydrogen Bond
ALA481 4.31833 Alkyl
MET656 4.14751 Alkyl
LYS478 4.56505 Alkyl
TYR486 4.61738 Pi-Alkyl
PHE494 5.45504 Pi-Alkyl
PHE494 4.55613 Pi-Alkyl
PHE494 5.38745 Pi-Alkyl
TYR660 4.66797 Pi-Alkyl
Tecovirimat (Standard) THR659 2.66676 Conventional Hydrogen Bond
THR659 3.21461 Conventional Hydrogen Bond
SER338 3.37023 Carbon Hydrogen Bond
ASP654 3.54365 Halogen (Fluorine)
GLU339 4.85536 Pi-Anion
LYS337 4.23601 Alkyl
LYS337 4.76512 Alkyl
TYR658 4.55079 Pi-Alkyl

The docked complex of palustric acid reveals a tightly constrained binding pose governed by a combination of key hydrogen bonding and non-polar contacts. Specifically, the terminal carboxylic acid group of palustric acid forms a robust conventional hydrogen bond with Phe494, while a supplementary carbon-hydrogen and pi-donor hydrogen interaction engages Thr659 to fix the orientation of the core scaffold. The rigid hydrophenanthrene ring system is predominantly stabilized through a hydrophobic network, establishing pi-pi T-shaped and pi-sigma interactions with Phe334 alongside alkyl and pi-alkyl contacts spanning Lys478 and Ala481. Furthermore, a supportive van der Waals pocket composed of Lys337, Ser338, Thr485, Tyr486, Gly482, Tyr660, and Met656 closely envelopes the molecule, reinforcing overall binding stability.

Neoabietic acid adopts a structurally complementary posture within the same active site cavity, establishing an overlapping interaction fingerprint. Like palustric acid, the primary polar driver is anchored through a direct conventional hydrogen bond between its carboxylic acid headgroup and Phe494. The extended conjugated ring system of neoabietic acid drives strong hydrophobic interactions, forming dense alkyl and pi-alkyl contacts with Lys478, Ala481, Tyr486, and Tyr660. The periphery of the compound is heavily shielded by van der Waals forces from surrounding residues including Lys337, Ser338, Gly482, Val493, Tyr496, Thr659, and Met656, contributing significant non-polar binding energy to the complex.

In contrast to the hydrophobic-driven occupancy of the resin acids, the clinical control tecovirimat binds through a unique interaction profile characterized by mixed polar, electrostatic, and halogen-mediated forces. Tecovirimat anchors to the catalytic site via dual conventional hydrogen bonds between its imide carbonyl framework and Thr659, supplemented by carbon-hydrogen contacts with Ser655. Crucially, its trifluoromethyl moiety forms a prominent halogen interaction with Asp654, which operates alongside a strong pi-anion electrostatic bridge with Glu339 and hydrophobic pi-alkyl interactions with Lys337 and Tyr658. Taken together, these comparative structural findings demonstrate that while tecovirimat relies heavily on specialized halogen and electrostatic bridges, palustric acid and neoabietic acid achieve robust binding affinity by pairing a conserved polar hinge at Phe494 with broad hydrophobic engagement across the Phe334, Lys478, and Tyr660 pocket face.

Following the analysis of DNA polymerase inhibition, molecular docking simulations were conducted against the E8L cell surface-binding protein to evaluate the structural interaction profiles of neoabietic acid and isocembrene relative to tecovirimat (Table 2 and Figure 3). The visual and atomic-level binding inspections indicate that both natural compounds insert into the principal binding cleft of E8L, engaging a combination of directional polar contacts and hydrophobic contacts across key cationic and aliphatic residues.

Figure 3. Intermolecular interactions in the molecular docking complexes of (A) neoabietic acid- E8L cell surface-binding protein in 3D, (B) neoabietic acid-E8L cell surface-binding protein in 2D, (C) isocembrene-E8L cell surface-binding protein in 3D, (D) isocembrene-E8L cell surface-binding protein in 2D, (E) tecovirimat-E8L cell surface-binding protein in 3D, and (F) tecovirimat-E8L cell surface-binding protein in 2D.

Figure 3. Intermolecular interactions in the molecular docking complexes of (A) neoabietic acid- E8L cell surface-binding protein in 3D, (B) neoabietic acid-E8L cell surface-binding protein in 2D, (C) isocembrene-E8L cell surface-binding protein in 3D, (D) isocembrene-E8L cell surface-binding protein in 2D, (E) tecovirimat-E8L cell surface-binding protein in 3D, and (F) tecovirimat-E8L cell surface-binding protein in 2D.

Table 2. Molecular docking non-bonded interactions against E8L obtained from BIOVIA Discovery Studio Client 2025 software.

Compound Name Residue Distance (Å) Interaction Type
Neoabietic acid Tyr219 1.86058 Conventional Hydrogen Bond
Leu42 5.28741 Alkyl
Leu42 5.24151 Alkyl
Lys41 3.83924 Alkyl
Leu42 4.24659 Alkyl
Lys41 4.73984 Alkyl
Lys41 4.94800 Alkyl
Arg220 4.01047 Alkyl
Isocembrene Arg220 4.42806 Alkyl
Lys41 4.35002 Alkyl
Leu42 4.04568 Alkyl
Tyr69 4.97640 Pi-Alkyl
Tecovirimat (Standard) Tyr104 3.44110 Conventional Hydrogen Bond; Halogen (Fluorine)
Tyr104 3.32210 Conventional Hydrogen Bond; Halogen (Fluorine)
His67 2.83454 Carbon Hydrogen Bond
His67 4.61445 Pi-Pi T-shaped
Arg220 5.24851 Alkyl
His67 4.64914 Pi-Alkyl

Neoabietic acid adopts a stably anchored conformation within the E8L binding domain driven by concurrent polar and non-polar interactions. The carboxylic acid group forms a conventional hydrogen bond with Tyr219, providing a primary directional anchor within the active site. Adjacent to this polar hinge, the fused tricyclic core of neoabietic acid forms extensive hydrophobic alkyl contacts with Lys41, Leu42, and Arg220. This binding posture is further stabilized by a van der Waals contact envelope comprising Tyr69, Ile174, Asn175, Glu217, and Asn218, which closely contours the hydrophobic framework of the ligand.

Isocembrene occupies the same binding domain of the E8L protein, relying on non-polar interaction forces. Owing to its neutral macrocyclic diterpene architecture, its bound pose is governed by hydrophobic contacts, forming alkyl and pi-alkyl interactions with Lys41, Leu42, Tyr69, and Arg220. The large macrocyclic ring structure is accommodated within the pocket through additional van der Waals interactions with Gly40, Glu217, Asn218, Tyr219, and Ile215.

The reference compound tecovirimat demonstrates an interaction footprint involving hydrogen bonding, carbon-hydrogen bonding, and aromatic contacts within the E8L pocket. Tecovirimat anchors to the site through conventional hydrogen bonds with Tyr104 and Thr173, supplemented by carbon-hydrogen interactions with His67 and Asn175. The central aromatic ring forms pi-pi T-shaped and pi-alkyl contacts with His67, while its terminal cage scaffold establishes alkyl interactions with Lys41 and Arg220. Surrounding residues, including Arg44, Tyr69, Val94, Ile116, Leu42, Ile174, Ser177, and Asn218, constitute an extensive van der Waals interaction network around the control ligand. Both neoabietic acid and isocembrene share the common hydrophobic contact locus at Lys41, Leu42, and Arg220 observed in the tecovirimat-E8L complex.

ADMET Properties Analysis

For evaluating the pharmacokinetic, physicochemical, and toxicological profiles of the lead compounds, in silico ADMET predictions were generated using SwissADME, Deep-PK, and ProTox 3.0, with tecovirimat and trifluridine serving as clinical reference controls (Table 3). Figure 4 represents the ADME radar, and Figure 5 depicts the charts prepared with the toxicity probability prediction data obtained from ProTox 3.0.

Table 3. ADMET data overview retrieved from SwissADME, Deep-PK, and ProTox 3.0.

Parameter Palustric acid Neoabietic acid Isocembrene Tecovirimat Trifluridine
Molecular Weight 302.45 302.45 272.47 376.33 296.2
TPSA 37.3 37.3 0 66.48 104.55
Consensus Log P 4.73 4.82 5.85 2.88 -0.51
ESOL Log S -4.41 -4.87 -5.26 -3.72 -2.85
GI absorption High High Low High High
BBB permeant Yes Yes No Yes No
P-glycoprotein substrate No No No No No
Lipinski violations 1 1 1 0 0
Ghose violations 0 0 1 0 0
Bioavailability Score 0.85 0.85 0.55 0.55 0.55
PAINS alerts 0 0 0 0 0
Synthetic accessibility 5.03 4.70 5.02 4.17 3.71
LD50 (mg/kg) 650 1000 5300 2028 10000
Toxicity Class 4 4 5 5 6
Caco-2 (log Papp) -4.47 -4.45 -4.44 -5.00 -5.88
CYP1A2 Inhibitor No (High) No (High) Yes (High) No (High) No (High)
CYP2C19 Inhibitor No (High) No (Medium) Yes (High) No (High) No (High)
CYP2C9 Inhibitor No (Low) No (Medium) No (High) No (High) No (High)
CYP2D6 Inhibitor No (High) No (High) No (High) No (High) No (High)
CYP3A4 Inhibitor No (High) No (High) No (High) No (Low) No (High)
Half-Life of Drug < 3 h (Medium) < 3 h (Medium) < 3 h (Low) ≥ 3 h (Low) < 3 h (Low)
AMES Mutagenesis Safe (High) Safe (High) Safe (High) Toxic (High) Toxic (High)
Abbreviations: MW, molecular weight; TPSA, topological polar surface area; LogP, octanol/water partition coefficient; LogS, aqueous solubility; GI, gastrointestinal; BBB, blood–brain barrier; P-gp, P-glycoprotein; LD₅₀, median lethal dose; Caco-2, Caco-2 cell permeability (log Papp); CYP, cytochrome P450; AMES, Ames mutagenicity assay. Values in parentheses for CYP inhibition, predicted half-life, and AMES mutagenicity indicate the confidence level of the computational prediction. Data source: Physicochemical and drug-likeness properties were predicted using SwissADME, acute toxicity (LD₅₀ and toxicity class) was obtained from ProTox-3.0, and Caco-2 permeability, CYP inhibition, predicted half-life, and AMES mutagenicity were predicted using Deep-PK. Brackets "()" in some values indicate the prediction probability.

Physicochemical evaluation (Table 1) shows that the abietane diterpenoids palustric acid and neoabietic acid exhibit identical molecular weights (302.45 g/mol) and topological polar surface areas (TPSA = 37.33 Ų), pairing high gastrointestinal (GI) absorption with blood-brain barrier (BBB) permeability. Both compounds display single Lipinski rule violations due to their lipophilicity (consensus Log P of 4.73 and 4.82, respectively), moderate water solubility (ESOL Log S of -4.41 and -4.87), high bioavailability scores (0.85), and zero PAINS alerts. Isocembrene exhibits the highest lipophilicity (consensus Log P = 5.85; ESOL Log S = -5.26) and TPSA of 0 Ų, resulting in low GI absorption, lack of BBB permeability, a single Lipinski violation, one Ghose violation, and a bioavailability score of 0.55. In comparison, tecovirimat and trifluridine demonstrate lower lipophilicity (consensus Log P of 2.88 and -0.51), zero Lipinski/Ghose violations, and TPSA values of 66.48 Ų and 104.55 Ų, respectively. None of the evaluated compounds act as P-glycoprotein substrates.

Figure 4. ADME radar of (A) Palustric acid, (B) Neoabietic acid, (C) Isocembrene, (D) Tecovirimat, and (E) Trifluridine, obtained from SwissADME.

Figure 4. ADME radar of (A) Palustric acid, (B) Neoabietic acid, (C) Isocembrene, (D) Tecovirimat, and (E) Trifluridine, obtained from SwissADME.

Cytochrome P450 (CYP) inhibition profiling indicates that palustric acid and neoabietic acid do not inhibit CYP1A2, CYP2C19, CYP2C9, CYP2D6, or CYP3A4, matching the non-inhibitory profiles of tecovirimat and trifluridine. Conversely, isocembrene acts as an inhibitor for both CYP1A2 and CYP2C19. All test natural products display estimated elimination half-lives of less than 3 hours, aligning with trifluridine (&lt;3 h) and contrasting with tecovirimat (≥3 h). Deep-PK toxicity modeling classifies palustric acid and neoabietic acid as Class 4 toxicants with predicted LD₅₀ values of 650 mg/kg and 1000 mg/kg, respectively, while isocembrene falls into Class 5 (LD₅₀ = 5300 mg/kg), outperforming tecovirimat (Class 5, LD₅₀ = 2028 mg/kg) and trifluridine (Class 6, LD₅₀ = 10000 mg/kg). AMES mutagenesis modeling classifies palustric acid, neoabietic acid, and isocembrene as safe, contrasting with the predicted mutagenic toxicity of both tecovirimat and trifluridine. Caco-2 permeability (logPaap) ranges from -4.44 to -4.47 for the three candidate ligands, compared to -5.00 for tecovirimat and -5.88 for trifluridine.

Figure 5. Toxicity charts with probability values prepared with the data retrieved from ProTox 3.0.

Figure 5. Toxicity charts with probability values prepared with the data retrieved from ProTox 3.0.

Moreover, organ toxicity, toxicity endpoints, and molecular initiating pathways of the candidate compounds were analyzed computationally relative to tecovirimat and trifluridine, as illustrated in Figure 5. In silico organ toxicity and general endpoint profiling (Figure 6A) demonstrate that palustric acid, neoabietic acid, and isocembrene exhibit low nephrotoxicity probabilities (0.25, 0.25, and 0.11, respectively), contrasting favorably with tecovirimat (0.35) and trifluridine (0.55). For neurotoxicity and respiratory toxicity, palustric acid and neoabietic acid show probabilities of 0.53 and 0.80, whereas isocembrene registers markedly lower values at 0.49 and 0.29. Hepatotoxicity scores for palustric acid, neoabietic acid, and tecovirimat are identical at 0.58, whereas isocembrene (0.20) and trifluridine (0.24) display reduced potential. Immunotoxicity potential remains minimal for neoabietic acid (0.07) and tecovirimat (0.04), whereas isocembrene exhibits an elevated score of 0.80. Crucially, across general toxicity endpoints, mutagenicity and carcinogenicity probabilities for palustric acid and neoabietic acid remain low (0.04 and 0.31), remaining well below the values observed for tecovirimat (0.46 mutagenicity; 0.59 carcinogenicity) and trifluridine (0.64 mutagenicity; 0.40 carcinogenicity).

Assessment of molecular initiating events and stress response pathways (Figure 6B) reveals near-zero activation of phosphoprotein p53 tumor suppressor signaling across all three candidate natural products (0.01), in sharp contrast to tecovirimat (0.27) and trifluridine (1.00). Interactions with GABA receptors yield probability scores of 0.60 for palustric acid and neoabietic acid, comparable to tecovirimat (0.61). Pregnane X receptor (PXR) engagement is uniform across palustric acid, neoabietic acid, isocembrene, and tecovirimat (0.50–0.51). For voltage-gated sodium channels (VGSC) and transthyretin (TTR), palustric acid and neoabietic acid display lower initiation probabilities (0.12 and 0.30) relative to tecovirimat (0.31 and 0.59). Cytochrome CYP2C9 metabolic interaction probability stands at 0.41 for both abietane acids, increasing to 0.71 for isocembrene and 0.51 for tecovirimat.

Discussion

The ongoing re-emergence of the mpox virus (MPXV) as a global public health threat highlights the urgent need for novel therapeutics with distinct mechanisms of action. Current clinical intervention strategies rely heavily on tecovirimat, an antiviral originally approved under the Animal Rule that targets the orthopoxvirus VP37 envelope protein (encoded by F13L) to block extracellular enveloped virion formation [46, 47]. However, the rapid emergence of resistant MPXV strains harboring single-point mutations within VP37 underscores the vulnerability of single-target monotherapies and necessitates the development of inhibitors targeting alternative, highly conserved machinery [48]. In this study, we targeted two indispensable nodes in the MPXV life cycle: the viral DNA polymerase, responsible for intracellular genomic replication, and the E8L cell surface-binding protein, which mediates viral attachment by recognizing cell-surface chondroitin sulfate glycosaminoglycans [69]. Previous studies in the scientific literature include interpretation of these two targets with various approaches [4953].

Structural validation of our AlphaFold-predicted E8L model confirmed high conformational reliability, supported by a pTM score of 0.78 and over 99% of residues occupying permitted regions of the Ramachandran plot. Molecular docking simulations revealed that the natural diterpenoids palustric acid, neoabietic acid, and isocembrene accommodate within the functional cavities of these target proteins through distinct binding modes. In DNA polymerase, palustric acid and neoabietic acid form a conserved polar anchor with Phe494 via their terminal carboxylic acid groups. This electrostatic interaction is complemented by extensive hydrophobic packing across Phe334, Lys478, Ala481, and Tyr660. While tecovirimat relies on halogen contacts with Asp654 and pi-anion bridges with Glu339, the diterpenoids match its binding stability by leveraging rigid hydrophenanthrene cores to drive hydrophobic interactions deep within the catalytic cleft.

A similar structural mechanism was observed at the host-entry axis targeting the E8L surface-binding protein. Both neoabietic acid and the macrocyclic diterpene isocembrene mapped to the primary attachment site, forming contacts with a positively charged basic residue cluster comprising Lys41, Leu42, and Arg220. In native viral attachment, electropositive surface patches on E8L facilitate electrostatic tethering to negatively charged host cell glycosaminoglycans. Occupancy of the Lys41/Arg220 site by neoabietic acid (anchored via a hydrogen bond to Tyr219) and isocembrene effectively neutralizes this critical binding domain. By occupying these residues, both natural lead compounds present a structural mechanism to disrupt initial virion attachment.

The therapeutic potential of these candidate inhibitors is further supported by their predicted ADMET and safety profiles. In silico pharmacokinetic profiling demonstrated high gastrointestinal absorption and favorable oral bioavailability scores (0.85) for palustric acid and neoabietic acid, outperforming the reference compounds tecovirimat and trifluridine in predicted Caco-2 cell permeability. Crucially, metabolic enzyme screening indicated that neither abietane acid inhibits major cytochrome P450 isoforms (CYP1A2, CYP2C19, CYP2C9, CYP2D6, or CYP3A4), suggesting a low risk for clinical drug-drug interactions. Toxicological risk assessment via ProTox 3.0 and Deep-PK further highlighted their favorable safety margins, showing non-mutagenic profiles in AMES modeling and minimal predicted activation of the p53 tumor suppressor stress pathway (0.01), contrasting with the mutagenic and p53-inducing profiles predicted for tecovirimat and trifluridine.

The structural and computational findings in this study identify the abietane diterpenoids palustric acid and neoabietic acid as promising multi-target lead candidates against MPXV. By combining direct catalytic inhibition of viral replication machinery with competitive blockade of the E8L host-attachment site, these compounds offer a compelling dual-action scaffold designed to lower the threshold for drug resistance. Further in vitro enzymatic inhibition assays and cell-based viral plaque reduction studies will be essential to validate these predicted mechanisms and advance these natural product scaffolds toward preclinical development.

Conclusion

This study demonstrates the potential of the abietane diterpenoids palustric acid and neoabietic acid as potent, multi-target lead compounds against the mpox virus (MPXV). By integrating high-confidence structural modeling of the E8L cell surface-binding protein with molecular docking against both E8L and the viral DNA polymerase, we uncovered a dual-mechanism computational profile. While tecovirimat relies on specialized halogen and electrostatic interactions, palustric acid and neoabietic acid effectively engage the DNA polymerase catalytic pocket by combining a conserved polar hinge at Phe494 with extensive hydrophobic packing across Phe334, Lys478, and Tyr660. Furthermore, at the host-entry axis, both neoabietic acid and isocembrene target the critical electropositive Lys41/Arg220 site on E8L, offering a structural rationale for blocking initial virion attachment to host cell glycosaminoglycans. Complementing their structural binding affinity, in silico ADMET and toxicity evaluations revealed that these abietane diterpenoids possess favorable oral bioavailability, high gastrointestinal absorption, lack of cytochrome P450 inhibition, and a superior safety profile—marked by non-mutagenicity and near-zero p53 stress pathway activation compared to reference antivirals. Taken together, these findings highlight palustric acid and neoabietic acid as compelling scaffolds that warrant further in vitro enzymatic, binding, and viral neutralization studies to advance the development of novel therapies against MPXV.

References

  1. J.S. Velásquez, F.B. Herrera-Echeverría, H.S. Porres-Paredes, C. Rodríguez-Cerdeira, Understanding the Epidemiology of Monkeypox Virus to Prevent Future Outbreaks, Microorganisms 12 (2024) 2576. https://doi.org/10.3390/microorganisms12122576.

  2. Mpox, (n.d.). https://www.who.int/news-room/fact-sheets/detail/mpox (accessed July 27, 2026).

  3. E.M. Bunge, B. Hoet, L. Chen, F. Lienert, H. Weidenthaler, L.R. Baer, R. Steffen, The changing epidemiology of human monkeypox—A potential threat? A systematic review, PLOS Neglected Tropical Diseases 16 (2022) e0010141. https://doi.org/10.1371/journal.pntd.0010141.

  4. S. Elsayed, L. Bondy, W.P. Hanage, Monkeypox Virus Infections in Humans, Clin Microbiol Rev 35 (n.d.) e00092-22. https://doi.org/10.1128/cmr.00092-22.

  5. A.J. Rodríguez-Morales, C. Luna, L. Flores-Girón, F.J. Membrillo de Novales, C. Torres-Martinez, G. Camacho-Moreno, R. Sah, J.D. Acosta-España, F. Amer, C. Espinal, J. Brea, M.L. Avila-Aguero, R. Ulloa-Gutierrez, J.A. Suárez, Mpox in children (2024): New Challenges, BMJ Paediatr Open 8 (2024) e003030. https://doi.org/10.1136/bmjpo-2024-003030.

  6. E. Lansiaux, N. Jain, S. Laivacuma, A. Reinis, The virology of human monkeypox virus (hMPXV): A brief overview, Virus Res 322 (2022) 198932. https://doi.org/10.1016/j.virusres.2022.198932.

  7. W. Li, Q. Yang, W. Xie, W. Dong, S. Chiu, An integrated review of monkeypox: from pathogen and epidemiology to diagnostics, control, and challenges, Emerging Microbes & Infections 15 (2026) 2695529. https://doi.org/10.1080/22221751.2026.2695529.

  8. A. Bogacka, A. Wroczynska, W. Rymer, P. Grzesiowski, R. Kant, M. Grzybek, M. Parczewski, Mpox unveiled: Global epidemiology, treatment advances, and prevention strategies, One Health 20 (2025) 101030. https://doi.org/10.1016/j.onehlt.2025.101030.

  9. P. Mishra, R. Singh, A. Patil, P. Mishra, R. Singh, A. Patil, Epidemiology, Pathogenesis, and Treatment Options of Monkeypox: A Narrative Review, Cureus 17 (2025). https://doi.org/10.7759/cureus.77892.

  10. Y. Pasikhova, Antiviral Agents: Cidofovir and Brincidofovir, in: C. Somboonwit, P. Shapshak, P. Kangueane, S. Balaji, J.T. Sinnott, L.J. Menezes, A. Oxner (Eds.), Global Virology IV: Viral Disease Diagnosis and Treatment Delivery in the 21st Century, Springer International Publishing, Cham, 2024: pp. 251–277. https://doi.org/10.1007/978-3-031-57369-9_18.

  11. N. Nasim, I.S. Sandeep, S. Mohanty, Plant-derived natural products for drug discovery: current approaches and prospects, Nucleus (Calcutta) 65 (2022) 399–411. https://doi.org/10.1007/s13237-022-00405-3.

  12. M. Ponticelli, M.L. Bellone, V. Parisi, A. Iannuzzi, A. Braca, N. de Tommasi, D. Russo, A. Sileo, P. Quaranta, G. Freer, M. Pistello, L. Milella, Specialized metabolites from plants as a source of new multi-target antiviral drugs: a systematic review, Phytochem Rev 22 (2023) 615–693. https://doi.org/10.1007/s11101-023-09855-2.

  13. M. Abdelsayed, AI-Driven Polypharmacology in Small-Molecule Drug Discovery, Int J Mol Sci 26 (2025) 6996. https://doi.org/10.3390/ijms26146996.

  14. J. Chen, Y. Zhao, X. Chen, Y. Li, L. Kang, Y. Liu, The antiviral properties of flavonoids, Clinical Traditional Medicine and Pharmacology 6 (2025) 200192. https://doi.org/10.1016/j.ctmp.2024.200192.

  15. Polyphenols in Health and Disease: Gut Microbiota, Bioaccessibility, and Bioavailability, (n.d.). https://www.mdpi.com/2673-6918/3/1/5 (accessed July 27, 2026).

  16. S. Prabhu, K. Kalaimathi, M. Thiruvengadam, M. Ayyanar, K. Shine, S. Amalraj, S.A. Ceasar, S.P. Priya, N. Prakash, Antiviral mechanisms of dietary polyphenols: recent developments as antiviral agents and future prospects in combating Nipah virus, Phytochem Rev 24 (2025) 3063–3099. https://doi.org/10.1007/s11101-024-10017-1.

  17. Terpenes and cannabidiol against human corona and influenza viruses–Anti-inflammatory and antiviral in vitro evaluation - PMC, (n.d.). https://pmc.ncbi.nlm.nih.gov/articles/PMC10840330/ (accessed July 27, 2026).

  18. R. Ancuceanu, M.V. Hovaneț, A. Miron, A.I. Anghel, M. Dinu, Phytochemistry, Biological, and Pharmacological Properties of Abies alba Mill., Plants (Basel) 12 (2023) 2860. https://doi.org/10.3390/plants12152860.

  19. E. Tavčar Benković, D. Žigon, V. Mihailović, T. Petelinc, P. Jamnik, S. Kreft, Identification, in vitro and in vivo Antioxidant Activity, and Gastrointestinal Stability of Lignans from Silver Fir (Abies alba) Wood Extract, Journal of Wood Chemistry and Technology 37 (2017) 467–477. https://doi.org/10.1080/02773813.2017.1340958.

  20. M. Sirše, S.K. Fokter, B. Štrukelj, J. Zupan, Silver Fir (Abies alba L.) Polyphenolic Extract Shows Beneficial Influence on Chondrogenesis In Vitro under Normal and Inflammatory Conditions, Molecules 25 (2020) 4616. https://doi.org/10.3390/molecules25204616.

  21. M.D. Vukić, N.L. Vuković, M.R. Jakovljević, M.S. Ristić, M. Kačániová, Antimicrobial Effects of Abies alba Essential Oil and Its Application in Food Preservation, Plants 14 (2025) 2071. https://doi.org/10.3390/plants14132071.

  22. A.M. Fahim, Advances in computer-aided drug design: From molecular docking to artificial intelligence-driven therapeutic discovery, ASPET Discovery 2 (2026) 100024. https://doi.org/10.1016/j.aspetd.2026.100024.

  23. X.-Y. Meng, H.-X. Zhang, M. Mezei, M. Cui, Molecular Docking: A powerful approach for structure-based drug discovery, Curr Comput Aided Drug Des 7 (2011) 146–157. https://doi.org/10.2174/157340911795677602.

  24. K. Saritha, M. Alivelu, M. Mohammad, Drug-likeness analysis, in silico ADMET profiling of compounds in Kedrostis foetidissima (Jacq.) Cogn, and antibacterial activity of the plant extract, In Silico Pharmacol 12 (2024) 67. https://doi.org/10.1007/s40203-024-00240-1.

  25. R.P. Vivek-Ananth, K. Mohanraj, A.K. Sahoo, A. Samal, IMPPAT 2.0: An Enhanced and Expanded Phytochemical Atlas of Indian Medicinal Plants, ACS Omega 8 (2023) 8827–8845. https://doi.org/10.1021/acsomega.3c00156.

  26. K. Mohanraj, B.S. Karthikeyan, R.P. Vivek-Ananth, R.P.B. Chand, S.R. Aparna, P. Mangalapandi, A. Samal, IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics, Sci Rep 8 (2018) 4329. https://doi.org/10.1038/s41598-018-22631-z.

  27. S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, Q. Li, B.A. Shoemaker, P.A. Thiessen, B. Yu, L. Zaslavsky, J. Zhang, E.E. Bolton, PubChem 2023 update, Nucleic Acids Research 51 (2023) D1373–D1380. https://doi.org/10.1093/nar/gkac956.

  28. M.M.F. Ismail, M.S. Ayoup, Review on fluorinated nucleoside/non-nucleoside FDA-approved antiviral drugs, RSC Adv. 12 (2022) 31032–31045. https://doi.org/10.1039/D2RA05370E.

  29. J. Cinatl, M. Bechtel, P. Reus, M. Ott, F. Rothweiler, M. Michaelis, S. Ciesek, D. Bojkova, Trifluridine for treatment of mpox infection in drug combinations in ophthalmic cell models, Journal of Medical Virology 96 (2024) e29354. https://doi.org/10.1002/jmv.29354.

  30. N.M. O’Boyle, M. Banck, C.A. James, C. Morley, T. Vandermeersch, G.R. Hutchison, Open Babel: An open chemical toolbox, J Cheminform 3 (2011) 33. https://doi.org/10.1186/1758-2946-3-33.

  31. Y. Shen, Y. Li, R. Yan, Structural basis for the inhibition mechanism of the DNA polymerase holoenzyme from mpox virus, Structure 32 (2024) 654-661.e3. https://doi.org/10.1016/j.str.2024.03.004.

  32. H.M. Berman, The Protein Data Bank, Nucleic Acids Research 28 (2000) 235–242. https://doi.org/10.1093/nar/28.1.235.

  33. The UniProt Consortium, A. Bateman, M.-J. Martin, S. Orchard, M. Magrane, A. Adesina, S. Ahmad, E.H. Bowler-Barnett, H. Bye-A-Jee, D. Carpentier, P. Denny, J. Fan, P. Garmiri, L.J.D.C. Gonzales, A. Hussein, A. Ignatchenko, G. Insana, R. Ishtiaq, V. Joshi, D. Jyothi, S. Kandasaamy, A. Lock, A. Luciani, J. Luo, Y. Lussi, J.S.M. Marin, P. Raposo, D.L. Rice, R. Santos, E. Speretta, J. Stephenson, P. Totoo, N. Tyagi, N. Urakova, P. Vasudev, K. Warner, S. Wijerathne, C.W.-H. Yu, R. Zaru, A.J. Bridge, L. Aimo, G. Argoud-Puy, A.H. Auchincloss, K.B. Axelsen, P. Bansal, D. Baratin, T.M. Batista Neto, M.-C. Blatter, J.T. Bolleman, E. Boutet, L. Breuza, B.C. Gil, C. Casals-Casas, K.C. Echioukh, E. Coudert, B. Cuche, E. De Castro, A. Estreicher, M.L. Famiglietti, M. Feuermann, E. Gasteiger, P. Gaudet, S. Gehant, V. Gerritsen, A. Gos, N. Gruaz, C. Hulo, N. Hyka-Nouspikel, F. Jungo, A. Kerhornou, P.L. Mercier, D. Lieberherr, P. Masson, A. Morgat, S. Paesano, I. Pedruzzi, S. Pilbout, L. Pourcel, S. Poux, M. Pozzato, M. Pruess, N. Redaschi, C. Rivoire, C.J.A. Sigrist, K. Sonesson, S. Sundaram, A. Sveshnikova, C.H. Wu, C.N. Arighi, C. Chen, Y. Chen, H. Huang, K. Laiho, M. Lehvaslaiho, P. McGarvey, D.A. Natale, K. Ross, C.R. Vinayaka, Y. Wang, J. Zhang, UniProt: the Universal Protein Knowledgebase in 2025, Nucleic Acids Research 53 (2025) D609–D617. https://doi.org/10.1093/nar/gkae1010.

  34. J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, A. Bridgland, C. Meyer, S.A.A. Kohl, A.J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M. Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A.W. Senior, K. Kavukcuoglu, P. Kohli, D. Hassabis, Highly accurate protein structure prediction with AlphaFold, Nature 596 (2021) 583–589. https://doi.org/10.1038/s41586-021-03819-2.

  35. J. Abramson, J. Adler, J. Dunger, R. Evans, T. Green, A. Pritzel, O. Ronneberger, L. Willmore, A.J. Ballard, J. Bambrick, S.W. Bodenstein, D.A. Evans, C.-C. Hung, M. O’Neill, D. Reiman, K. Tunyasuvunakool, Z. Wu, A. Žemgulytė, E. Arvaniti, C. Beattie, O. Bertolli, A. Bridgland, A. Cherepanov, M. Congreve, A.I. Cowen-Rivers, A. Cowie, M. Figurnov, F.B. Fuchs, H. Gladman, R. Jain, Y.A. Khan, C.M.R. Low, K. Perlin, A. Potapenko, P. Savy, S. Singh, A. Stecula, A. Thillaisundaram, C. Tong, S. Yakneen, E.D. Zhong, M. Zielinski, A. Žídek, V. Bapst, P. Kohli, M. Jaderberg, D. Hassabis, J.M. Jumper, Accurate structure prediction of biomolecular interactions with AlphaFold 3, Nature 630 (2024) 493–500. https://doi.org/10.1038/s41586-024-07487-w.

  36. R.A. Laskowski, M.W. MacArthur, J.M. Thornton, PROCHECK : validation of protein-structure coordinates, F (2006) 695–743. https://doi.org/10.1107/97809553602060000724.

  37. Schrödinger, LLC, The PyMOL Molecular Graphics System, Version 1.8, (2015).

  38. L. Polák, P. Škoda, K. Riedlová, R. Krivák, M. Novotný, D. Hoksza, PrankWeb 4: a modular web server for protein–ligand binding site prediction and downstream analysis, Nucleic Acids Research 53 (2025) W466–W471. https://doi.org/10.1093/nar/gkaf421.

  39. Python: a programming language for software integration and development - PubMed, (n.d.). https://pubmed.ncbi.nlm.nih.gov/10660911/ (accessed July 28, 2026).

  40. PROTEINS: Structure, Function, and Bioinformatics | Protein Science Journal | Wiley Online Journal, (n.d.). https://onlinelibrary.wiley.com/doi/full/10.1002/%28SICI%291097-0134%2819990701%2936%3A1%3C1%3A%3AAID-PROT1%3E3.0.CO%3B2-T (accessed July 28, 2026).

  41. O. Trott, A.J. Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, J Comput Chem 31 (2010) 455–461. https://doi.org/10.1002/jcc.21334.

  42. A. Daina, O. Michielin, V. Zoete, SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules, Sci Rep 7 (2017) 42717. https://doi.org/10.1038/srep42717.

  43. Y. Myung, A.G.C. de Sá, D.B. Ascher, Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction, Nucleic Acids Research 52 (2024) W469–W475. https://doi.org/10.1093/nar/gkae254.

  44. P. Banerjee, E. Kemmler, M. Dunkel, R. Preissner, ProTox 3.0: a webserver for the prediction of toxicity of chemicals, Nucleic Acids Research 52 (2024) W513–W520. https://doi.org/10.1093/nar/gkae303.

  45. Y. Zhang, J. Skolnick, Scoring function for automated assessment of protein structure template quality, Proteins: Structure, Function, and Bioinformatics 57 (2004) 702–710. https://doi.org/10.1002/prot.20264.

  46. S.M. Hoy, Tecovirimat: First Global Approval, Drugs 78 (2018) 1377–1382. https://doi.org/10.1007/s40265-018-0967-6.

  47. C.E. DeLaurentis, J. Kiser, J. Zucker, New Perspectives on Antimicrobial Agents: Tecovirimat for Treatment of Human Monkeypox Virus, Antimicrob Agents Chemother 66 (n.d.) e01226-22. https://doi.org/10.1128/aac.01226-22.

  48. J.M. Garrigues, P. Hemarajata, A. Espinosa, J.K. Hacker, N.T. Wynn, T.G. Smith, C.M. Gigante, W. Davidson, J. Vega, H. Edmondson, A. Karan, A.N. Marutani, M. Kim, D. Terashita, S.E. Balter, C.L. Hutson, N.M. Green, Community spread of a human monkeypox virus variant with a tecovirimat resistance-associated mutation, Antimicrob Agents Chemother 67 (n.d.) e00972-23. https://doi.org/10.1128/aac.00972-23.

  49. V.K. Maurya, S. Kumar, S. Maurya, S. Ansari, J.T. Paweska, A.S. Abdel-Moneim, S.K. Saxena, Structure-based drug designing for potential antiviral activity of selected natural product against Monkeypox (Mpox) virus and its host targets, Virusdisease 35 (2024) 589–608. https://doi.org/10.1007/s13337-024-00900-y.

  50. H. Tiwari, A. Ilyas, P.K. Rai, S. Upadhyay, S. Borkotoky, Computational investigation of antiviral peptide interactions with Mpox DNA polymerase, In Silico Pharmacol 13 (2025) 49. https://doi.org/10.1007/s40203-025-00342-4.

  51. M. Amjid, M.M. Khan, S.F. Pastore, J.B. Vincent, T. Muhammad, Computational screening of antiviral candidates for Monkeypox virus DNA polymerase and A42R protein, PLOS Neglected Tropical Diseases 19 (2025) e0013312. https://doi.org/10.1371/journal.pntd.0013312.

  52. R. Das, A. Bhattarai, R. Karn, B. Tamang, Computational investigations of potential inhibitors of monkeypox virus envelope protein E8 through molecular docking and molecular dynamics simulations, Sci Rep 14 (2024) 19585. https://doi.org/10.1038/s41598-024-70433-3.

  53. H. Abbou, R. Zegrari, Z. Gaouzi, L. Belyamani, I. Bourais, R. Eljaoudi, Trans-Cannabitriol as a Dual Inhibition of MPOX Adhesion Receptors L1R and E8L: An In Silico Perspective, Bioinform Biol Insights 19 (2025) 11779322251355315. https://doi.org/10.1177/11779322251355315.