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

Manuilov, Vladimir; Francés-Monerris, Antonio; Abdelgawwad, Abdelazim M.A.; Escudero, Daniel; Makarov, Ilya

doi:10.24963/ijcai.2025/1269

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

Vladimir Manuilov, Antonio Francés-Monerris, Abdelazim M.A. Abdelgawwad, Daniel Escudero, Ilya Makarov

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Demo Track. Pages 11091-11094.

https://doi.org/10.24963/ijcai.2025/1269

Keywords

Multidisciplinary Topics and Applications: MTA: Physical sciences
Machine Learning: ML: Generative models
Machine Learning: ML: Reinforcement learning
Multidisciplinary Topics and Applications: MTA: Energy, environment and sustainability

Abstract

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.

Vladimir Manuilov, Antonio Francés-Monerris, Abdelazim M.A. Abdelgawwad, Daniel Escudero, and Ilya Makarov. 2025. Machine Learning Driven Optimization of Fe-Based TMCs for Photodynamic Therapy. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 11091–11094. https://doi.org/10.24963/ijcai.2025/1269

@inproceedings{ijcai2025p1269,
  title     = {Machine Learning Driven Optimization of Fe-Based TMCs for Photodynamic Therapy},
  author    = {Manuilov, Vladimir and Francés-Monerris, Antonio and Abdelgawwad, Abdelazim M.A. and Escudero, Daniel and Makarov, Ilya},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {11091--11094},
  year      = {2025},
  month     = {8},
  note      = {Demo Track},
  doi       = {10.24963/ijcai.2025/1269},
  url       = {https://doi.org/10.24963/ijcai.2025/1269},
}

TY  - CPAPER
TI  - Machine Learning Driven Optimization of Fe-Based TMCs for Photodynamic Therapy
AU  - Manuilov, Vladimir
AU  - Francés-Monerris, Antonio
AU  - Abdelgawwad, Abdelazim M.A.
AU  - Escudero, Daniel
AU  - Makarov, Ilya
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 11091
EP  - 11094
DO  - 10.24963/ijcai.2025/1269
UR  - https://doi.org/10.24963/ijcai.2025/1269
ER  -