Hope Zehr, Alberto Baiardi, Francesco Tacchino, Anthony Gandon, Laurin E Fischer, Yue Xu, Frank P DiFilippo, Leonardo Guidoni, Pi A B Haase, Walter N Talarico, Martina Stella, Fabio Tarocco, Anton Nykänen, Aaron Fitzpatrick, Aaron Miller, Leander Thiessen, Stefan Knecht, Elsi-Mari Borrelli, Sabrina Maniscalco, Fabijan Pavošević, Ivano Tavernelli, Edward Maytin, Vijay Krishna
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Quantum Computing for Photosensitizer Design in Photodynamic Therapy.
Use of light in healthcare is evolving with increasing applications of photodynamic therapy (PDT) for treating various cancers. PDT utilizes light-activated molecules called photosensitizers (PSs) that generate reactive oxygen species (ROSs) to induce tumor cell apoptosis and necrosis. However, the use of PDT is limited by the availability of PSs that can be activated by deep tissue-penetrating near-infrared light, exhibit low dark toxicity, and produce ROSs efficiently. Here we review the different categories of PS currently used in clinical or preclinical trials and highlight the significance of advanced computational methods, including density functional and wave function-based quantum chemistry, for understanding the molecular mechanisms involved in PS activation. Despite advancements in classical computational techniques, the complexities of excited state dynamics in highly correlated molecular systems demand innovative simulation approaches such as quantum computing. We propose that quantum computing holds promise for accurately modeling the excited-state properties of PSs to optimize their design and broaden clinical applications.
期刊介绍:
The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.