ABSTRACT:
Building upon the previously published Allicin Digital Twin v2.0 (Morais et al., IJRPAS 2026), this study presents Digital Twin v3.0 — a five-layer computational framework that integrates, for the first time, real molecular dynamics (MD) trajectory data from Discovery Studio CHARMm36 simulations (Li & Cheng, 2023) for the alliin–6LU7 and allicin–6LU7 complexes into an adaptive multi-scale model. Layer 4 assimilates the RMSD and RMSF trajectories from Li & Cheng, establishing a structural calibration anchor for the SARS-CoV-2 main protease (Mpro, PDB: 6LU7) active pocket (HIS41, CYS44, MET49, PRO52, TYR54, MET165, ASP187, ARG188, GLN189, GLN192). Layer 5 introduces two novel metrics: the Nanocage Enhancement Factor for Inhibition (NEFI) and the Stability–Binding Integrated Score (SBIS), derived from a Hill–Langmuir pharmacodynamic model coupled to the ODE degradation engine. The Allicin/Al₁₂N₁₂ complex achieves NEFI = 14.0× and SBIS = 320.7 relative to free allicin against Mpro. Integration of real MD data reduced the predicted mean RMSF in the Mpro active pocket from 1.088 Å (allicin–6LU7, Li & Cheng) to a predicted 0.163 Å for Allicin/Al₁₂N₁₂, a 6.66-fold reduction consistent with the 44-fold B-factor decrease already established in v2.0. The Digital Twin framework's Gaussian Process surrogate (RMSD equilibrium: 0.485 Å, SASA: 6.96 nm²) is validated against the real CHARMm36 RMSD plateau of 1.10 nm for alliin–6LU7. The study closes the gap between static DFT thermodynamics and time-resolved binding dynamics, while identifying in vitro stability assays for Allicin/Al₁₂N₁₂ under physiological conditions as the critical next experimental step.