TY - JOUR

T1 - Quantitative Structure-Property Relationship Modelling for the Prediction of Singlet Oxygen Generation by Heavy-Atom-Free BODIPY Photosensitizers

AU - Buglak, Andrey A

AU - Charisiadis, Asterios

AU - Sheehan, Aimee

AU - Kingsbury, Christopher J

AU - Senge, Mathias O

AU - Filatov, Mikhail A

N1 - Funding Information: This work was prepared with the support of funding from the European Union's Horizon 2020 research and innovation programme under the FET‐OPEN grant agreement No.828779 and the Technical University of Munich – Institute for Advanced Study through a Hans Fischer Senior Fellowship. M.A.F. and A.S. acknowledge the TU Dublin Research Scholarship programme for support of this work. Open access funding enabled and organized by Projekt DEAL. Publisher Copyright: © 2021 The Authors. Chemistry - A European Journal published by Wiley-VCH GmbH.

PY - 2021/7/7

Y1 - 2021/7/7

N2 - Heavy-atom-free sensitizers forming long-living triplet excited states via the spin-orbit charge transfer intersystem crossing (SOCT-ISC) process have recently attracted attention due to their potential to replace costly transition metal complexes in photonic applications. The efficiency of SOCT-ISC in BODIPY donor-acceptor dyads, so far the most thoroughly investigated class of such sensitizers, can be finely tuned by structural modification. However, predicting the triplet state yields and reactive oxygen species (ROS) generation quantum yields for such compounds in a particular solvent is still very challenging due to a lack of established quantitative structure-property relationship (QSPR) models. In this work, the available data on singlet oxygen generation quantum yields (phi(Delta)) for a dataset containing >70 heavy-atom-free BODIPY in three different solvents (toluene, acetonitrile, and tetrahydrofuran) were analyzed. In order to build reliable QSPR model, a series of new BODIPYs were synthesized that bear different electron donating aryl groups in the meso position, their optical and structural properties were studied along with the solvent dependence of singlet oxygen generation, which confirmed the formation of triplet states via the SOCT-ISC mechanism. For the combined dataset of BODIPY structures, a total of more than 5000 quantum-chemical descriptors was calculated including quantum-chemical descriptors using density functional theory (DFT), namely M06-2X functional. QSPR models predicting phi Delta values were developed using multiple linear regression (MLR), which perform significantly better than other machine learning methods and show sufficient statistical parameters (R=0.88-0.91 and q(2)=0.62-0.69) for all three solvents. A small root mean squared error of 8.2 % was obtained for phi(Delta) values predicted using MLR model in toluene. As a result, we proved that QSPR and machine learning techniques can be useful for predicting phi Delta values in different media and virtual screening of new heavy-atom-free BODIPYs with improved photosensitizing ability.

AB - Heavy-atom-free sensitizers forming long-living triplet excited states via the spin-orbit charge transfer intersystem crossing (SOCT-ISC) process have recently attracted attention due to their potential to replace costly transition metal complexes in photonic applications. The efficiency of SOCT-ISC in BODIPY donor-acceptor dyads, so far the most thoroughly investigated class of such sensitizers, can be finely tuned by structural modification. However, predicting the triplet state yields and reactive oxygen species (ROS) generation quantum yields for such compounds in a particular solvent is still very challenging due to a lack of established quantitative structure-property relationship (QSPR) models. In this work, the available data on singlet oxygen generation quantum yields (phi(Delta)) for a dataset containing >70 heavy-atom-free BODIPY in three different solvents (toluene, acetonitrile, and tetrahydrofuran) were analyzed. In order to build reliable QSPR model, a series of new BODIPYs were synthesized that bear different electron donating aryl groups in the meso position, their optical and structural properties were studied along with the solvent dependence of singlet oxygen generation, which confirmed the formation of triplet states via the SOCT-ISC mechanism. For the combined dataset of BODIPY structures, a total of more than 5000 quantum-chemical descriptors was calculated including quantum-chemical descriptors using density functional theory (DFT), namely M06-2X functional. QSPR models predicting phi Delta values were developed using multiple linear regression (MLR), which perform significantly better than other machine learning methods and show sufficient statistical parameters (R=0.88-0.91 and q(2)=0.62-0.69) for all three solvents. A small root mean squared error of 8.2 % was obtained for phi(Delta) values predicted using MLR model in toluene. As a result, we proved that QSPR and machine learning techniques can be useful for predicting phi Delta values in different media and virtual screening of new heavy-atom-free BODIPYs with improved photosensitizing ability.

KW - BODIPY

KW - machine learning

KW - photosensitization

KW - structure-property relationship

KW - singlet oxygen

KW - TRIPLET EXCITED-STATES

KW - INTRAMOLECULAR CHARGE-TRANSFER

KW - ACTIVITY-RELATIONSHIPS QSARS

KW - PHOTODYNAMIC THERAPY

KW - PHOTOREDOX CATALYSIS

KW - ELECTRON-DONOR

KW - PORPHYRIN

KW - ACCEPTOR

KW - DYES

KW - DERIVATIVES

KW - structure–property relationship

UR - http://www.scopus.com/inward/record.url?scp=85106505364&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/8cb87ed1-f891-3fbc-a38b-70f7250bf585/

U2 - 10.1002/chem.202100922

DO - 10.1002/chem.202100922

M3 - статья

C2 - 33876842

VL - 27

SP - 9934

EP - 9947

JO - Chemistry - A European Journal

JF - Chemistry - A European Journal

SN - 0947-6539

IS - 38

ER -