TY - JOUR

T1 - Singlet Oxygen Generation by Porphyrins and Metalloporphyrins Revisited: a Quantitative Structure-Property Relationship (QSPR) Study

AU - Buglak, Andrey A.

AU - Filatov, Mikhail A.

AU - Hussain, M. Althaf

AU - Sugomoto, Manabu

PY - 2020/12/1

Y1 - 2020/12/1

N2 - Porphyrins and metalloporphyrins are used as photosensitizers in photocatalysis, photodynamic therapy (PDT), disinfection, degradation of persistent pollutants and other applications. Their mechanism of action involves intersystem crossing to triplet excited state followed by formation of singlet oxygen (1O2), which is a highly reactive species and mediates various oxidative processes. The design of advanced sensitizers based on porphyrin compounds have attracted significant attention in recent years. However, it is still difficult to predict the efficiency of singlet oxygen generation for a given structure. Our goal was to develop a quantitative structure-property relationship (QSPR) model for the fast virtual screening and prediction of singlet oxygen quantum yields for pophyrins and metalloporphyrins. We performed QSPR analysis of a dataset containing 32 compounds, including various porphyrins and their analogues (chlorins and bacteriochlorins). Quantum-chemical descriptors were calculated using Density Functional Theory (DFT), namely B3LYP and M062X functionals. Three different machine learning methods were used to develop QSPR models: random forest regression (RFR), support vector regression (SVR), and multiple linear regression (MLR). The optimal QSPR model «structure – singlet oxygen generation quantum yield» obtained using RFR method demonstrated high determination coefficient for the training set (R2 = 0.949) and the highest predicting ability for the test set (pred_R2 = 0.875). This proves that the developed QSPR method is realiable and can be directly applied in the studies of singlet oxygen generation both for free base porphyrins and their metal complexes. We believe that QSPR approach developed in this study can be useful for the search of new poprhyrin photosensitizers with enhanced singlet oxygen generation ability.

AB - Porphyrins and metalloporphyrins are used as photosensitizers in photocatalysis, photodynamic therapy (PDT), disinfection, degradation of persistent pollutants and other applications. Their mechanism of action involves intersystem crossing to triplet excited state followed by formation of singlet oxygen (1O2), which is a highly reactive species and mediates various oxidative processes. The design of advanced sensitizers based on porphyrin compounds have attracted significant attention in recent years. However, it is still difficult to predict the efficiency of singlet oxygen generation for a given structure. Our goal was to develop a quantitative structure-property relationship (QSPR) model for the fast virtual screening and prediction of singlet oxygen quantum yields for pophyrins and metalloporphyrins. We performed QSPR analysis of a dataset containing 32 compounds, including various porphyrins and their analogues (chlorins and bacteriochlorins). Quantum-chemical descriptors were calculated using Density Functional Theory (DFT), namely B3LYP and M062X functionals. Three different machine learning methods were used to develop QSPR models: random forest regression (RFR), support vector regression (SVR), and multiple linear regression (MLR). The optimal QSPR model «structure – singlet oxygen generation quantum yield» obtained using RFR method demonstrated high determination coefficient for the training set (R2 = 0.949) and the highest predicting ability for the test set (pred_R2 = 0.875). This proves that the developed QSPR method is realiable and can be directly applied in the studies of singlet oxygen generation both for free base porphyrins and their metal complexes. We believe that QSPR approach developed in this study can be useful for the search of new poprhyrin photosensitizers with enhanced singlet oxygen generation ability.

KW - Porphyrins

KW - Photosensitization

KW - Singlet oxygen

KW - Quantitative structure-property relationship

KW - machine learning

KW - Machine learning

UR - https://www.sciencedirect.com/science/article/abs/pii/S1010603020306316

UR - http://www.scopus.com/inward/record.url?scp=85089215331&partnerID=8YFLogxK

U2 - 10.1016/j.jphotochem.2020.112833

DO - 10.1016/j.jphotochem.2020.112833

M3 - Article

VL - 403

JO - Journal of Photochemistry and Photobiology A: Chemistry

JF - Journal of Photochemistry and Photobiology A: Chemistry

SN - 1010-6030

M1 - 112833

ER -