%0 Journal Article
%T Mechanistic Digital Twins of Drug–Target Dynamics for Counterfactual Perturbation, Adaptive Simulation, and Uncertainty-Aware Molecular Decision-Making
%A Andrei Popescu
%A Mihai Ionescu
%A Elena Stan
%A Cristina Radu
%J International Journal of Pharmaceutical Research and Allied Sciences
%@ 2277-3657
%D 2026
%V 15
%N 1
%R 10.51847/cDo7KLqMC1
%P 123-132
%X Molecular simulation can characterize conformational ensembles, energy landscapes, ligand interactions, and transition pathways, yet an individual simulation remains a conditional representation rather than a continuously updated molecular counterpart. This article proposes an original mechanistic digital-twin architecture for drug–target dynamics that separates latent molecular state, intervention, physical time, evidence-update time, decision horizon, and observation. The architecture links a persistent entity and provenance record with mechanistic transition models, dynamic conformational-state estimation, counterfactual ligand, mutation, and environmental perturbations, adaptive simulation, and active information acquisition. Uncertainty is propagated across sampling, force fields, parameters, observations, software implementations, learned components, and competing mechanistic hypotheses rather than compressed into a single confidence value. Counterfactual branches are evaluated against structural, thermodynamic, kinetic, and functional consequences, with validation requirements determined by the intended context of use. A governance layer distinguishes exploratory hypothesis generation from bounded molecular decision support and requires escalation, revision, or deferral when provenance, identifiability, validation, or uncertainty conditions are inadequate. The original contribution is the integration of established molecular-simulation methods into a traceable state–intervention–observation–update architecture rather than the proposal of a new simulation algorithm. The framework is intended to generate testable propositions and organize evidence acquisition, but it does not establish predictive validity, causal therapeutic effects, clinical usefulness, regulatory acceptability, universal applicability, or deployment readiness. Its value therefore depends on prospective, context-specific computational and experimental validation. 
%U https://ijpras.com/article/mechanistic-digital-twins-of-drugtarget-dynamics-for-counterfactual-perturbation-adaptive-simulati-qhgxwi8mzoi9fjm