A Unified Framework for Biomolecular Design Based on Thermodynamic Efficiency and Information Fidelity: The Quantum–Thermodynamic–Informational (QTI) Paradigm

The Quantum–Thermodynamic–Informational (QTI) framework integrates quantum electronic structure, stochastic thermodynamics, and algorithmic information theory to derive the evolutionary optimization of biomolecular systems from first principles. Central to QTI is the Functional Thermodynamic Efficiency (FTE) principle, formalized as $\eta_{\text{func}} = \frac{\mathcal{F}{\text{output}}}{\dot{S}{\text{prod}} + \lambda , \mathcal{E}_{\text{error}}}$, a variational extremum grounded in fluctuation theorems and relative entropy minimization. Detailed derivations, including quantum-augmented thermodynamic uncertainty relations (TUR), elucidate FTE's emergence from path-integral formulations. Multi-scale simulations using Psi4, OpenMM, and PyG, validated against PDB and BRENDA datasets, confirm FTE’s predictions for protein folding, enzymatic kinetics, and replication fidelity. Novel applications in precision medicine and genetic engineering—including FTE-optimized CRISPR guides, base editing, and cancer therapies targeting metabolic inefficiencies—are benchmarked against 2024–2025 data. Comparative analyses, Pareto fronts, and sensitivity assessments underscore QTI’s rigor, with reproducible workflows ensuring verifiability. QTI establishes a predictive foundation for physics-constrained synthetic biology, precision medicine, and genome editing.