BUILDING-UP AND EVALUATING A DATABASE OF DYES FOR PHOTODYNAMIC THERAPY
Adequate photosensitizers have high photochemical, electrochemical, and thermal stability, which gives them diverse technological applications, such as in solar cells and photodynamic therapy (PDT). PDT is used in the treatment of cancer and other non-oncological diseases. In this treatment, photosensitizers are used that interact with light so that radical species are generated inside the cell, causing damage to the cell membrane, DNA, RNA, and/or proteins that can lead to cell death. In the search for new dyes with better pharmacokinetic properties, computational methods are of great importance, since it is possible to obtain the properties of the molecules that predict their use as a drug. In this work, the Density Functional Theory (DFT) method is used, as it proves to be a very advantageous tool for the design of new molecules, due to its relatively low computational cost and high capacity for prediction of electronic properties in good agreement with experimental results. Additionally, this work aims to use the Machine Learning technique to optimize the proposal of new compounds. An algorithm will be trained to propose, through a database with a large number of molecules, new photosensitizers tuned with the desired properties, especially in relation to the prediction of the electronic absorption spectrum.