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Published on: October 28, 2015
DFT-ML-Based Property Prediction of Transition Metal Complex Photosensitizers for Photodynamic Therapy
Jingxing Gao1, Yachao Dong1, Tian Qiu1
1School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
Abstract:
Photodynamic therapy (PDT) is a noninvasive clinical treatment for cancers using photosensitizers and light. While most research has focused on organic molecules, such as porphyrins as photosensitizers, there is emerging interest in the utilization of transition metal complexes (TMCs). Photosensitizer synthesis and the following performance test are time- and resource-consuming, so presynthetic screening of photosensitizers for their property would be critical. In this work, a hybrid mechanistic and data-driven model is proposed for the quantitative structure-property relationship (QSPR) of photosensitizers; important excited-state quantum chemistry descriptors (e.g., excitation energy) are first calculated based on density functional theory (DFT), and these descriptors, together with other molecular descriptors, are used to build single and hybrid machine learning (ML) models for the prediction of the singlet oxygen quantum yield of hexacoordinate TMC photosensitizers (Ru-, Ir-, and Re-complex). The support vector regression model and kernel ridge regression model are shown to provide good predictions on test (R 2 > 0.9) and external test sets (R 2 > 0.7) in single-ML models, while the delta-learning model and the Mixture-of-Experts model can further improve the generalization ability (R 2 up to 0.87 on the external test set) and show strong universality. SHAP analysis further confirms the reasonable choice of the mechanistic descriptors in the QSPR model. To our knowledge, this constitutes the first integrated DFT-ML framework specifically designed for the unique challenges of small data sets in TMC photosensitizer research.
Insights
This study introduces a hybrid quantum chemistry and machine learning model to predict the performance of transition metal complex photosensitizers for photodynamic therapy. The framework accurately screens novel photosensitizers, accelerating cancer treatment research.
Area of Science:
- Photodynamic Therapy
- Computational Chemistry
- Materials Science
Background:
- Photodynamic therapy (PDT) utilizes photosensitizers and light for cancer treatment, with growing interest in transition metal complexes (TMCs).
- Synthesizing and testing photosensitizers is resource-intensive, necessitating predictive screening methods.
- Existing quantitative structure-property relationship (QSPR) models often lack the specificity for TMC photosensitizers.
Purpose of the Study:
- To develop and validate a hybrid mechanistic and data-driven QSPR model for predicting the singlet oxygen quantum yield of hexacoordinate TMC photosensitizers.
- To integrate quantum chemistry descriptors with machine learning for efficient presynthetic screening.
- To establish an integrated DFT-ML framework for TMC photosensitizer research, addressing small dataset challenges.
Main Methods:
- Density functional theory (DFT) calculations to obtain excited-state quantum chemistry descriptors (e.g., excitation energy).
- Development of single and hybrid machine learning (ML) models, including support vector regression, kernel ridge regression, delta-learning, and Mixture-of-Experts.
- Utilizing descriptors from DFT and molecular properties to train ML models for predicting singlet oxygen quantum yield.
- SHAP analysis to interpret model predictions and validate descriptor relevance.
Main Results:
- Single ML models (SVR, KRR) achieved high prediction accuracy (R² > 0.9 on test sets, R² > 0.7 on external test sets).
- Hybrid models (delta-learning, MoE) demonstrated improved generalization (R² up to 0.87 on external test sets) and universality.
- SHAP analysis confirmed the mechanistic relevance of chosen quantum chemistry descriptors.
- The study presents the first integrated DFT-ML framework tailored for TMC photosensitizer research with limited data.
Conclusions:
- The developed hybrid DFT-ML framework provides accurate and reliable predictions for TMC photosensitizer performance.
- This approach significantly enhances the efficiency of discovering novel photosensitizers for photodynamic therapy.
- The framework offers a robust solution for QSPR modeling in areas with small experimental datasets, like TMC photosensitizer research.
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