Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning DOI Creative Commons
Vincenzo Vigna, Tânia Cova, Alberto A. C. C. Pais

et al.

Journal of Cheminformatics, Journal Year: 2025, Volume and Issue: 17(1)

Published: Jan. 5, 2025

Effective light-based cancer treatments, such as photodynamic therapy (PDT) and photoactivated chemotherapy (PACT), rely on compounds that are activated by light efficiently, absorb within the therapeutic window (600–850 nm). Traditional prediction methods for these absorption properties, including Time-Dependent Density Functional Theory (TDDFT), often computationally intensive time-consuming. In this study, we explore a machine learning (ML) approach to predict in region of platinum, iridium, ruthenium, rhodium complexes, aiming at streamlining screening potential photoactivatable prodrugs. By compiling dataset 9775 complexes from Reaxys database, trained six classification models, random forests, support vector machines, neural networks, utilizing various molecular descriptors. Our findings indicate Extreme Gradient Boosting Classifier (XGBC) paired with AtomPairs2D descriptors delivers highest predictive accuracy robustness. This ML-based method significantly accelerates identification suitable compounds, providing valuable tool early-stage design development phototherapy drugs. The also allows change relevant structural characteristics base molecule using information supervised approach. Scientific Contribution: proposed predicts ability transition metal-based UV–vis window, key trait phototherapeutic agents. While ML models have been used properties organic molecules, applying metal is novel. model efficient, fast, resource-light, decision tree-based algorithms provide interpretable results. interpretability helps understand rules facilitates targeted modifications convert inactive into potentially active ones.

Language: Английский

Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning DOI Creative Commons
Vincenzo Vigna, Tânia Cova, Alberto A. C. C. Pais

et al.

Journal of Cheminformatics, Journal Year: 2025, Volume and Issue: 17(1)

Published: Jan. 5, 2025

Effective light-based cancer treatments, such as photodynamic therapy (PDT) and photoactivated chemotherapy (PACT), rely on compounds that are activated by light efficiently, absorb within the therapeutic window (600–850 nm). Traditional prediction methods for these absorption properties, including Time-Dependent Density Functional Theory (TDDFT), often computationally intensive time-consuming. In this study, we explore a machine learning (ML) approach to predict in region of platinum, iridium, ruthenium, rhodium complexes, aiming at streamlining screening potential photoactivatable prodrugs. By compiling dataset 9775 complexes from Reaxys database, trained six classification models, random forests, support vector machines, neural networks, utilizing various molecular descriptors. Our findings indicate Extreme Gradient Boosting Classifier (XGBC) paired with AtomPairs2D descriptors delivers highest predictive accuracy robustness. This ML-based method significantly accelerates identification suitable compounds, providing valuable tool early-stage design development phototherapy drugs. The also allows change relevant structural characteristics base molecule using information supervised approach. Scientific Contribution: proposed predicts ability transition metal-based UV–vis window, key trait phototherapeutic agents. While ML models have been used properties organic molecules, applying metal is novel. model efficient, fast, resource-light, decision tree-based algorithms provide interpretable results. interpretability helps understand rules facilitates targeted modifications convert inactive into potentially active ones.

Language: Английский

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