Predicting fluorescence to singlet oxygen generation quantum yield ratio for BODIPY dyes using QSPR and machine learning
Platon P. Chebotaev, Andrey A. Buglak, Aimee Sheehan, Mikhail A. Filatov
Abstract
= 0.73-0.91) for both polar and non-polar media. The relative contributions of the descriptors to the models were assessed, identifying Eig03_EA(dm), F01[C-N], and TDB06p as the most influential. These results demonstrate that QSPR machine learning methods are effective in predicting key photochemical parameters of BODIPY photosensitizers, thereby potentially streamlining the development of theranostic agents.
Topics & Concepts
Singlet oxygenQuantum yieldBODIPYYield (engineering)Quantitative structure–activity relationshipFluorescencePhotochemistryChemistryOxygenSinglet stateComputational chemistryMaterials scienceExcited stateOrganic chemistryPhysicsStereochemistryAtomic physicsOpticsMetallurgyLuminescence and Fluorescent MaterialsNanoplatforms for cancer theranosticsAdvanced Fluorescence Microscopy Techniques