Machine Learning Driven Optimization of Fe-Based TMCs for Photodynamic Therapy

Noble metal-based photoactive complexes have applications in photodynamic therapy (PDT), but their toxicity and high cost drive interest in sustainable and cheaper alternatives like iron-based compounds. In this paper, quantum chemistry and classical molecular dynamics were employed to characterize the photophysical properties and non-covalent interactions with DNA of two Fe(III) complexes. We explained the absorption of IR wavelength by bright ligand-to-metal transitions and showed that the complexes exhibit persistent, albeit modest, interaction with DNA. Building on these traditional simulation methods, we propose a conceptual ML-driven optimization module designed to refine the structure of iron complexes and enhance their photophysical features. While the framework is not yet implemented, we demonstrate that key properties relevant for PDT can be computationally evaluated, providing a foundation for future iterative optimization. The ML module integrates 3D molecular structures, simulation results, and quantum chemical insights to suggest modifications aimed at shifting the absorption spectrum more favorably into the visible range, improving their suitability for phototherapies.