Predicting Simultaneous Blockade of Viral Entry and Replication Pathways by Digital Twin v3.1: Validation of the DTIS and SVEI Indices for the Allicin/Al₁₂N₁₂ Nanocomplex

Jefferson Lorençoni de Morais^1,2*, Heliel Gabriel Borges de Sena³, Larissa Neres Barbosa³ , Lanna Araújo Gomes³

1^* Polytechnic School of the Alves Faria University Center — UNIALFA, Goiânia, Brazil.

2^* American Global Technology University – AGTU, Orlando, EUA.                    

3 Institute of Phamaceutical and Exact Sciences – UNIGOIÁS, Goiás, Brazil.

 

*Correspondence: Jefferson.morais@unialfa.com.br;

DOI: https://doi.org/10.71431/IJRPAS.2026.5607    

Article Information		Abstract
Research Article Received: 08/06/2026 Accepted: 20/06/2026 Published:30/06/2026   Keywords Allicin; Digital Twin; SARS-CoV-2; Al₁₂N₁₂; Molecular Dynamics.		Extending the Digital Twin v3.0 published by Morais et al. (IJRPAS, May/2026), this work presents Digital Twin v3.1 — a seven-layer computational framework that expands the Mpro (6LU7) inhibition model into a dual-target paradigm, simultaneously incorporating the S/ACE2 protein (PDB: 6M17) as the second antiviral target of SARS-CoV-2. Layer 6 integrates real CHARMm36 molecular dynamics (MD) data from Li & Cheng (2023) for the alliin–6M17 and allicin–6M17 complexes: neither equilibrates over 100 conformations (in contrast to 6LU7, where alliin equilibrates at ~50 conformations); the mean RMSF in the 17-residue S/ACE2 interface active pocket is 0.857 Å (alliin) and 1.128 Å (allicin), a difference of 0.271 Å (24.9%). Layer 7 introduces three novel metrics: DTIS (Dual-Target Inhibition Score), DTAUC and SVEI (SARS-CoV-2 Viral Entry Inhibition). The Allicin/Al₁₂N₁₂ complex achieves NEFI_dual = 14.8×, DTAUC = 42.03 days·inhib. (vs. 2.84 for free allicin), Peak DTIS = 70.1%, and EDc = −8.10 kcal/mol for S/ACE2 — a more negative binding energy than for Mpro itself (−6.74 kcal/mol). The ACE2/Mpro ratio = 1.20 indicates structural preference of the nanocomplex for the S/ACE2 interface, suggesting that the primary mechanism of action of Allicin/Al₁₂N₁₂ may be viral entry blockade. The Digital Twin-predicted RMSF for Allicin/Al₁₂N₁₂ at the S/ACE2 interface is 0.169 Å — a 6.66-fold reduction below free allicin. The framework remains adaptive: new experimental data (in vitro stability, real GROMACS MD, ADMET) can recalibrate the model within minutes.

INTRODUCTION

Background and Motivation

Allicin (diallyl thiosulfinate, C₆H₁₀OS₂; CAS 539-86-6) is the principal bioactive compound of Allium sativum (garlic), with extensively documented anticancer, anti-inflammatory, antiviral, and antibacterial activities ^[1,2]. Its principal pharmacological limitation is rapid degradation in aqueous media (t½ ≈ 3.5–4 days at 37°C, pH 7.4), which severely restricts therapeutic bioavailability ^[3,4]. In response to this limitation, the series of studies published by our group has progressively developed a multi-scale Digital Twin (DT) framework to model and overcome this instability through nanocomplexes with Al₁₂N₁₂, B₁₂N₁₂, and C₂₄.

   Digital Twin v2.0 (Morais et al., IJRPAS, April/2026) ^[5] presented the first three-layer framework for modeling allicin aqueous degradation, parametrized by DFT data from Mozafari et al. (2025) ^[6], achieving a Nanocage Protection Factor (NPF) of 5,498,905× for the Al₁₂N₁₂ complex — representing an extension of the half-life from 3.6 days to more than 9,999 days. Version 3.0 (Morais et al., IJRPAS, May/2026) ^[7] expanded the framework to five layers, integrating real CHARMm36 MD data from Li & Cheng (2023) ^[8] for the Mpro target (6LU7) and introducing the NEFI (14.0×) and SBIS (320.7) metrics. The present work — Digital Twin v3.1 — completes the dual-target vision explicitly identified in v3.0 as the next step: simultaneously modeling Mpro inhibition and S/ACE2 interface blockade (6M17), integrating the MD data available in Li & Cheng ^[8] for both targets.

Importance of the S/ACE2 Target and the Dual-Target Approach

The SARS-CoV-2 Spike protein (S) mediates viral entry into host cells by binding to the human ACE2 receptor. Blocking this interface (PDB: 6M17, resolution 2.90 Å ^[9]) represents a complementary antiviral mechanism distinct from Mpro inhibition: while Mpro inhibits replication in already-infected cells, S/ACE2 blockade prevents infection of new cells. A compound acting simultaneously on both targets therefore operates at two distinct points of the viral cycle, reducing the likelihood of resistance and potentiating the therapeutic effect synergistically ^[10,11]. Li & Cheng (2023) ^[8] demonstrated that both alliin and allicin bind the 6M17 active pocket, with alliin exhibiting a LibDock score of 124.1 (vs. 103.2 for allicin) and forming three hydrogen bonds (ASP499, TRP165, LYS481) in addition to multiple salt bridges — a consistently more stable binding profile than allicin.

   Vieira et al. (2023) ^[12] developed VisualDynamics, a web platform that automates GROMACS simulations via a graphical interface, demonstrating that the computational barrier for MD is progressively being democratized. In this context, Digital Twin v3.1 is positioned as a computationally accessible framework that simulates dual-target pharmacodynamic behavior without requiring dedicated GPU resources, while maintaining full traceability and recalibrability.

Objectives

This study aims to: (i) integrate real CHARMm36 MD data from Li & Cheng (2023) for 6M17 as Layer 6 of the Digital Twin; (ii) develop Layer 7 — a dual-target pharmacodynamic model yielding the DTIS, DTAUC, and SVEI metrics; (iii) quantify NEFI_dual and compare nanocomplex binding affinity for Mpro (6LU7) vs. S/ACE2 (6M17); (iv) predict the active-pocket RMSF for the Allicin/Al₁₂N₁₂ complex at the 6M17 interface; and (v) identify the central scientific question motivating the next study in the series.

MATERIALS AND METHODS

Digital Twin v3.1 Architecture

 Digital Twin v3.1 fully inherits all layers of v3.0 ^[7] and introduces two new ones, as summarized in Table 1. Implementation was carried out in Python 3.12 (NumPy 1.26, SciPy 1.13, scikit-learn 1.4) with fixed seeds for reproducibility (numpy.random.seed(42)).

Table 1. Seven-layer architecture of Digital Twin v3.1.

Layer	Name	Data Source	Primary Output
1	Thermodynamic ODE Engine	DFT: Mozafari et al. 2025 [6]	Degradation kinetics, NPF, t½
2	GP MD Surrogate	Synthetic physics-based grid	RMSD, Rg, SASA, B-factor trajectories
3	Experimental Validation	Miron 2000; Lawson 2005; Block 1992	R²=0.9997; MAPE=10.3%; IR <1.18%
4	Real MD — 6LU7 (v3.0)	Li & Cheng 2023 [8] — CHARMm36	RMSD/RMSF anchor for Mpro active pocket
5	Mpro Binding Dynamics (v3.0)	Layers 1–4 + Hill–Langmuir	NEFI, SBIS, inhibition AUC
6 (NEW)	Real MD — 6M17 (v3.1)	Li & Cheng 2023 [8] — CHARMm36	RMSD/RMSF anchor for S/ACE2 active pocket; 17 interface residues
7 (NEW)	Dual-Target Model (v3.1)	Layers 1–6; synergistic model	DTIS, DTAUC, SVEI, NEFI_dual

 

Layer 6 — Integration of Real MD Data for 6M17

  Li & Cheng (2023) ^[8] performed molecular docking (DS LibDock) followed by MD simulation (Discovery Studio 2019, CHARMm36 force field, explicit solvent) for the alliin–6M17 and allicin–6M17 complexes. The simulation protocol comprised five stages: Minimization 1, Minimization 2, Heating, Equilibration, and Production (100 conformations in the RMSD output). RMSD and RMSF trajectories were digitized from Figures 7 and 8 of Li & Cheng (2023) and encoded as calibration targets in Digital Twin v3.1.

  Key structural features extracted from Li & Cheng data for 6M17 and incorporated as calibration anchors:

•         Alliin–6M17: RMSD in continuous fluctuation (2.2–3.6 nm); three hydrogen bonds (ASP499, TRP165, LYS481); multiple salt bridges (ARG169, LYS481, TRP271, PHE452, TRP477); van der Waals forces with TRP271.

•         Allicin–6M17: RMSD in continuous and wider fluctuation; only one hydrogen bond (ARG169) and one salt bridge (TRP477 via sulfur); hydrophobic interactions with TRP165, ALA264, TRP271, PHE452, LEU456, LEU503.

•         Neither complex equilibrates over 100 conformations — a fundamental contrast with 6LU7, where alliin equilibrates after ~50 conformations.

•         Stable region: residues 0–600; unstable region: 600–750 (protein larger than 6LU7, ~750 residues).

•         Mean RMSF of active pocket (17 residues): alliin = 0.857 Å; allicin = 1.128 Å; Δ = 0.271 Å (alliin 24.9% more stable).

Layer 7 — Dual-Target Pharmacodynamic Model

Dual-Target Inhibition Score (DTIS)

  The DTIS quantifies simultaneous inhibition of both targets as a function of time:

  where w_Mpro = 0.55 (weight of replication inhibition), w_ACE2 = 0.45 (weight of entry blockade), and γ = 0.30 (synergy coefficient, representing the superadditive cooperative effect of simultaneously blocking both viral mechanisms). The synergy factor is biologically grounded in viral clearance models demonstrating that the combination of entry blockade and replication inhibition is superadditive ^[10].

NEFI_dual — Nanocage Enhancement Factor for Inhibition (Dual-Target)

  NEFI_dual quantifies the dual-target therapeutic sustainability advantage conferred by nanocage encapsulation, combining binding affinity for both targets and half-life extension in a single metric.

SVEI — SARS-CoV-2 Viral Entry Inhibition Index

  The SVEI represents the S/ACE2 blockade fraction over time, isolating the viral entry contribution to total inhibition:

  This index is particularly relevant as a preventive metric — high SVEI indicates efficacy in preventing infection of new cells.

 

Docking Data for 6M17 — Nanocomplexes

  Binding energies EDc for free allicin and alliin at 6M17 were derived from Li & Cheng (2023) ^[8] LibDock scores by equivalent conversion. EDc values for nanocomplexes (Allicin/C₂₄, B₁₂N₁₂, and Al₁₂N₁₂) against 6M17 were estimated by applying the improvement factors observed at 6LU7 (Mozafari et al. ^[6]) with a conservative 78% correction for the larger S/ACE2 pocket (distinct geometry and hydrophobicity). The RMSF prediction for Allicin/Al₁₂N₁₂ at the 6M17 active pocket used the same B-factor reduction factor from the v2.0 surrogate (44-fold → RMSF factor 6.66×) applied to the allicin–6M17 reference value.

RESULTS AND DISCUSSION

Layer 6 — Integration of Real MD Data for 6M17

RMSD Analysis

  Figure 2 presents the RMSD trajectories for both targets. The behavior of 6M17 is fundamentally distinct from 6LU7: neither complex (alliin or allicin) equilibrates over 100 conformations. The physical reason is direct: 6M17 is an S/ACE2 dimer with ~750 residues, substantially larger than the monomeric Mpro (~306 residues), resulting in an RMSD fluctuation window of 2.2–3.6 nm — more than twice the values observed at 6LU7. Nonetheless, alliin–6M17 maintains consistently lower fluctuation than allicin–6M17, reflecting its denser interaction network (3 H-bonds + 3 salt bridges) versus allicin's single hydrogen bond.

  This observation carries an important implication for the Digital Twin: while for 6LU7 the v2.0 GP surrogate predicts rapid equilibration for Al₁₂N₁₂ (justified by high |Ead| = 40.28 kcal/mol), for 6M17 the equilibration prediction will be slower — expected given that the S/ACE2 interface pocket is topologically more open and the larger protein distributes thermal fluctuations across more residues.

Figure 2. (a) RMSD trajectories for alliin–6LU7 and allicin–6LU7. Alliin equilibrates at ~1.10 nm after conformation 50; allicin in continuous fluctuation. (b) RMSD trajectories for alliin–6M17 and allicin–6M17: neither equilibrates (6M17 is a dimer, ~750 residues). Data: Li & Cheng (2023) [8], CHARMm36, Discovery Studio.

RMSF Analysis — 6M17 Active Pocket

  Figure 3 compares RMSF at the active pockets of 6LU7 (12 residues) and 6M17 (17 residues). For 6M17, the residues with the largest alliin–allicin differential are: TRP477 (Δ = 0.35 Å) and PHE452 (Δ = 0.33 Å) — which compose the deep hydrophobic cavity of the S/ACE2 interface. Notably, the mean pocket RMSF for 6M17 for allicin (1.128 Å) is 3.7% larger than for 6LU7 (1.088 Å), consistent with the greater flexibility of the S/ACE2 interface compared to the compact Mpro catalytic pocket.

  The Digital Twin prediction for Allicin/Al₁₂N₁₂ at the 6M17 pocket is 0.169 Å — virtually identical to the 6LU7 prediction (0.163 Å), since both derive from the same B-factor reduction factor (6.66×). This is physically coherent: the stabilizing effect of the Al₁₂N₁₂ nanocage on allicin is independent of the protein target — it is a property of the ligand–nanocage complex itself.

Figure 3. (a) RMSF per residue in the 6LU7 active pocket (12 residues). (b) RMSF per residue in the 6M17 active pocket (17 residues). Orange bars = alliin; red = allicin; purple = Digital Twin prediction for Allicin/Al₁₂N₁₂ (6.66× reduction). MD data: Li & Cheng 2023 ^[8].

Table 2. S/ACE2 active pocket RMSF (6M17) - Comparative summary.

Residue	Index	Alliin (Å)	Allicin (Å)	ΔAllicin−Alliin (Å)	Al₁₂N₁₂ pred. (Å)
TRP165	165	0.92	1.18	0.26	0.177
ARG169	169	0.88	1.14	0.26	0.171
TRP271	271	0.91	1.20	0.29	0.180
PHE452	452	0.95	1.28	0.33	0.192
TRP477	477	0.97	1.32	0.35*	0.198
LYS481	481	0.85	1.12	0.27	0.168
ASP499	499	0.80	1.08	0.28	0.162
VAL506	506	0.83	1.10	0.27	0.165
Mean (17 res.)	—	0.857	1.128	0.271 (24.9%↑)	0.169

* TRP477: largest alliin–allicin differential in the S/ACE2 pocket. Al₁₂N₁₂ prediction: 6.66× reduction factor (derived from v2.0 surrogate B-factor). MD data: Li & Cheng 2023 ^[8].

Layer 7 — Dual-Target Model Results

NEFI_dual and DTAUC

  Table 3 and Figure 4 present the complete Layer 7 results. The Allicin/Al₁₂N₁₂ complex achieves NEFI_dual = 14.8× and DTAUC = 42.03 inhibition-days, representing 14.8 times more sustained dual-target inhibition than free allicin over 60 days. This result marginally exceeds the single-target NEFI (Mpro only) of 14.0× reported in v3.0 ^[7], owing to the added S/ACE2 component: the high affinity EDc = −8.10 kcal/mol for 6M17 elevates the Peak DTIS from 53.1% (free allicin) to 70.1% (Al₁₂N₁₂), contributing the synergy fraction.

  An unexpected result of high theoretical relevance is the ACE2/Mpro ratio pattern. For all analyzed complexes, binding affinity for the S/ACE2 target exceeds that for Mpro (ratio > 1.0). For Allicin/Al₁₂N₁₂, this ratio is 1.20, indicating that the nanocomplex has a structural preference for the S/ACE2 interface. This suggests that the primary mechanism of action of Allicin/Al₁₂N₁₂ may be viral entry blockade — a hypothesis with clinically relevant implications, since a preventive agent (entry blockade) has a distinct prophylactic application from a therapeutic agent (replication inhibition).

Table 3. Layer 7 — Complete dual-target metrics for all nanocomplexes (T = 37°C, pH 7.4, 60 days).

Species	DTAUC	NEFI_dual (×)	Mpro AUC	ACE2 AUC	Peak DTIS (%)	EDc ACE2 (kcal/mol)
Free Allicin	2.84	1.0	2.46	2.89	53.1	−5.15
Allicin/C₂₄	18.82	6.6	16.39	17.92	64.1	−6.82
Allicin/B₁₂N₁₂	27.24	9.6	21.49	27.67	45.4	−4.28
Allicin/Al₁₂N₁₂	42.03	14.8	34.45	37.10	70.1	−8.10

DTAUC = area under the DTIS curve (inhibition-days, 0–60 days); NEFI_dual = DTAUC(species)/DTAUC(free allicin); Peak DTIS = maximum value at t = 0 (%); EDc ACE2 derived from Li & Cheng 2023 [8] with scaling from Mozafari 2025 ^[6].

 

Figure 4. (a) Dual-Target Inhibition Score (DTIS, %) curves over 60 days for the four complexes. Allicin/Al₁₂N₁₂ and B₁₂N₁₂ maintain essentially constant inhibition; free allicin decays to zero in ~20 days. (b) NEFI_dual per species — Al₁₂N₁₂ achieves 14.8× superiority over free allicin.

AUC Comparison by Target and Binding Energies

  Figure 5 contrasts inhibition AUCs for Mpro versus S/ACE2 and the EDc binding energies. Consistently, all complexes show AUC_ACE2 > AUC_Mpro — except Allicin/C₂₄ — reflecting that S/ACE2 target affinity is systematically higher. This observation is coherent with the interaction scale reported by Li & Cheng ^[8] for 6M17: the S/ACE2 interface pocket is topologically more open and accommodates more extensive polar and hydrophobic interactions than the compact Mpro catalytic pocket.

 

Figure 5. (a) Sustained inhibition AUC: solid bars = Mpro (6LU7); hatched bars = S/ACE2 (6M17). For Al₁₂N₁₂, AUC_ACE2 (37.10) > AUC_Mpro (34.45). (b) Binding energies EDc per target: Al₁₂N₁₂ achieves −8.10 kcal/mol at S/ACE2, exceeding its own Mpro value (−6.74 kcal/mol). ACE2/Mpro ratio = 1.20.

SVEI — Viral Entry Inhibition and Multi-Metric Superiority

  Figure 6(a) shows the SVEI curves over time. Allicin/Al₁₂N₁₂ maintains a maximum blockade of ~62% over the S/ACE2 interface essentially constantly for 60 days, while free allicin decays to negligible blockade in ~25 days. Panel (b) synthesizes the multi-metric superiority of Al₁₂N₁₂ relative to free allicin: 14.8× in NEFI_dual, 14.8× in DTAUC, 15.2× in Mpro AUC, and 12.8× in ACE2 AUC.

  The most robust result of Layer 7 is that Allicin/Al₁₂N₁₂ dominates across all pharmacodynamic metrics — single-target (NEFI = 14.0× from v3.0 ^[7]) and dual-target (NEFI_dual = 14.8× from this work). The margin of superiority over Allicin/B₁₂N₁₂ (NEFI_dual = 9.6×) is amplified by the fact that Al₁₂N₁₂ combines the highest binding affinity with the highest thermodynamic stability. Allicin/C₂₄, despite its intermediate affinity, presents NEFI_dual of only 6.6× owing to its modest kinetic protection (NPF = 7.0×).

Figure 6. (a) SVEI — Viral Entry Inhibition (S/ACE2 blockade, %) over 60 days. Al₁₂N₁₂ maintains ~62% entry blockade constantly. (b) Multi-metric superiority: all metrics normalized to Free Allicin = 1.0×. Al₁₂N₁₂ exceeds free allicin by 14.8× in DTAUC and 14.8× in NEFI_dual.

Figure 1. (a) ODE aqueous degradation kinetics for free allicin and nanocomplexes at T = 37°C, pH 7.4. Dotted vertical lines indicate half-life points for free allicin (3.6 days) and Allicin/C₂₄ (25 days); B₁₂N₁₂ and Al₁₂N₁₂ complexes are effectively stable throughout. (b) Nanocage Protection Factor (NPF) on log₁₀ scale — Al₁₂N₁₂ achieves NPF = 5,498,905×. Layer 1 calibrated Ea = 17.75 kcal/mol.

Contextualization with Literature

  Li & Cheng (2023) ^[8] concluded that alliin exhibits stronger binding than allicin at both targets (6LU7 and 6M17), with LibDock scores of 124.1 vs. 98.7 for Mpro and 124.1 vs. 103.2 for S/ACE2. The present work contextualizes this finding: although alliin is a superior ligand, allicin encapsulated in Al₁₂N₁₂ achieves EDc = −8.10 kcal/mol at S/ACE2 — surpassing the estimated equivalent for alliin (−6.10 kcal/mol) — while delivering sustained temporal exposure that neither free compound can provide. This is the second consecutive case in the series where Al₁₂N₁₂ nanocomplexation reverses the potency hierarchy, converting a moderately potent, kinetically unstable natural compound into a candidate with pharmaceutical-grade stability and superior affinity.

  Vieira et al. (2023) ^[12] demonstrated that MD simulations via GROMACS can be performed by non-specialist users through the VisualDynamics platform. This methodological democratization is directly relevant to the next step in this series: real MD for the Allicin/Al₁₂N₁₂ and Allicin/B₁₂N₁₂ nanocomplexes could be executed via VisualDynamics without dedicated HPC infrastructure, making GP surrogate experimental validation accessible. Digital Twin v3.1 provides exactly the data needed to configure these simulations: initial structures (Ead, geometric parameters), temperature and pH conditions, and quantitative predictions of RMSD, Rg, SASA, and B-factor to be validated or refuted.

LIMITATIONS AND FUTURE DIRECTIONS

Limitations of the Present Work

  EDc values for nanocomplexes at 6M17 are estimates based on conservative scaling of the Mozafari et al. ^[6] results for 6LU7, not on direct molecular docking against the 6M17 structure. The binding hierarchy (Al₁₂N₁₂ > C₂₄ > free allicin > B₁₂N₁₂) is consistent across targets, but absolute values for 6M17 should be treated as first approximations. Additionally, the synergy coefficient γ = 0.30 in the DTIS is physically motivated but not calibrated against experimental dual-target viral clearance data.

Critical Next Step: Direct Docking against 6M17

  The natural next computational step in this series is direct molecular docking of the Al₁₂N₁₂, B₁₂N₁₂, and C₂₄ nanocomplexes against the 6M17 structure (PDB: 6M17, resolution 2.90 Å) using AutoDock Vina or Glide, providing EDc values calibrated directly for S/ACE2. This would replace the Layer 6 estimates with computed values and allow Layer 7 recalibration with experimental precision.

Real GROMACS MD for the Nanocomplexes

  The GP surrogate must ultimately be retrained against real GROMACS all-atom MD trajectories for Allicin/Al₁₂N₁₂ — prioritarily for 6LU7, then 6M17. The VisualDynamics platform ^[12] represents an accessible route for executing these simulations without dedicated HPC infrastructure. The Digital Twin quantitative predictions (RMSD_eq = 0.485 Å, RMSF = 0.163–0.169 Å, SASA = 6.96 nm²) are the primary hypotheses to be validated or refuted at this stage.

In Vitro Stability Assays

  The NPF = 5,498,905× for Al₁₂N₁₂ is a thermodynamic upper bound derived from DFT ΔG. UV-Vis kinetic degradation assays (242 nm) of the Allicin/Al₁₂N₁₂ complex in aqueous solution (37°C, pH 7.4) represent the most urgent and direct experimental validation in the entire series.

Extension to Alliin Nanocomplexes

  Given alliin's demonstrated superior binding to both targets (Li & Cheng ^[8]), DFT characterization of alliin/Al₁₂N₁₂, alliin/B₁₂N₁₂, and alliin/C₂₄ would complete the candidate space and potentially reveal NEFI_dual values even higher than those reported here for allicin.

CONCLUSION

 Digital Twin v3.1 presents, for the first time in the literature, a seven-layer computational framework for modeling the simultaneous dual-target inhibition of SARS-CoV-2 Mpro (6LU7) and S/ACE2 (6M17) by nanocage-encapsulated allicin complexes. Integration of real CHARMm36 MD data from Li & Cheng (2023) for 6M17 (Layer 6) revealed that neither alliin–6M17 nor allicin–6M17 complexes equilibrate over 100 conformations — behavior contrasting with 6LU7, where alliin equilibrates. The mean RMSF of the 17-residue S/ACE2 interface active pocket is 24.9% larger for allicin (1.128 Å) than for alliin (0.857 Å), with TRP477 exhibiting the largest differential (Δ = 0.35 Å).

  Layer 7 introduces three novel metrics — DTIS, DTAUC, and SVEI — that quantify sustained dual-target inhibition. The Allicin/Al₁₂N₁₂ complex dominates all indicators: NEFI_dual = 14.8×, DTAUC = 42.03 inhibition-days (vs. 2.84 for free allicin), Peak DTIS = 70.1%, and EDc_ACE2 = −8.10 kcal/mol — the most negative binding energy among all candidates at any target. The EDc_ACE2/EDc_Mpro ratio of 1.20 suggests structural preference of the nanocomplex for the S/ACE2 interface, positioning Al₁₂N₁₂ potentially as a preventive agent (entry blockade) in addition to a therapeutic one (replication inhibition).

  Digital Twin v3.1 closes the dual-target gap explicitly identified in v3.0, while opening the next critical question: are the estimated docking values for nanocomplexes at 6M17 confirmed by direct calculation? The answer, via AutoDock Vina/Glide against the 6M17 structure, is the objective of the next study in this series.

CONFLICT OF INTEREST

  The authors declare no conflict of interest.

ACKNOWLEDGEMENTS

  The authors thank the Polytechnic School of UNIALFA and the Institute of Pharmaceutical Sciences of UNIGOIAS for institutional support. This study was conducted entirely with open-source and no-cost computational resources, consistent with the Digital Twin framework's design for accessibility. The authors acknowledge the MD work of Li & Cheng (ISAIMS 2023) and the DFT work of Mozafari, Baei, and Tazikeh Lemeski (Sci. Rep. 2025), fundamental to this series of studies.

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