Quantum-mechanical insights into sodium alginate gelation and AI-driven discovery of synthetic biomimetic analogs

The functional versatility of sodium alginate (SA) in biomedical engineering is fundamentally governed by its sequence-dependent interaction with metal ions; however, the molecular mechanisms underlying these interactions remain incompletely understood. Herein, we establish a comprehensive molecular-level framework for SA and its synthetic biomimetic analogs by integrating density functional theory (DFT), artificial intelligence (AI)-assisted molecular design, molecular docking, molecular dynamics (MD) simulation, and MM-PBSA free energy analysis. Quantum chemical calculations revealed that β-D-mannuronic acid is slightly more stable than α-L-guluronic acid, while progressive polymerization and Na⁺ coordination significantly enhanced structural stability and reduced the HOMO–LUMO gap from 10.75 to 10.96 eV in monomers to 8.15 eV in SA, accompanied by increased softness (0.123 eV −1 ) and electrophilicity (2.031 eV). Theoretical FTIR spectra closely matched experimental observations, validating the optimized computational model. Guided by these insights, AI-assisted screening identified synthetic biomimetic scaffolds with substantially improved physicochemical and pharmacokinetic properties. Among the designed candidates, Analog 5 emerged as the closest structural mimic of SA, exhibiting reduced molecular weight (234.20 g mol −1 ), lower topological polar surface area (102.29 Å 2 ), zero Lipinski violations, and enhanced predicted bioavailability. Molecular docking against Aurora kinase A (AURKA) demonstrated the strongest binding for SA (Glide score = − 6.142; IFD score = − 11.23), followed by Analog 5 (− 5.405; − 7.97), Analog 4 (− 4.496; − 7.77), and Analog 3 (− 4.147; − 7.10). Subsequent 300 ns MD simulations confirmed stable ligand–protein complexes throughout the simulation, while MM-PBSA calculations revealed thermodynamically favorable binding free energies of − 666.260 ± 1.291 kJ mol −1 for SA, − 232.137 ± 0.772 kJ mol −1 for Analog 3, and − 47.735 ± 0.315 kJ mol −1 for Analog 5. Collectively, this integrated computational framework demonstrates that AI-designed biomimetic analogs successfully balance favorable molecular recognition with improved drug-like properties, providing a rational platform for developing next-generation synthetic alginate-inspired biomaterials for drug delivery, wound healing, tissue engineering, and regenerative medicine.