Lung surfactant as a biophysical assay for inhalation toxicology

Inhaled chemicals can cause dysfunction in the lung surfactant, a protein−lipid complex with critical biophysical and biochemical functions. This inhibition has many structure-related and dose-dependent mechanisms, making hazard identification challenging. We developed quantitative structure−activity relationships for predicting lung surfactant inhibition using machine learning. Logistic regression, support vector machines, random forest, gradient-boosted trees, prior-data-fitted networks, and multilayer perceptron were evaluated as methods. Multilayer perceptron had the strongest performance with 96% accuracy and an F1 score of 0.97. Support vector machines and logistic regression also performed well with lower computation costs. This serves as a proof-of-concept for efficient hazard screening in the emerging area of lung surfactant inhibition.

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Lung surfactant as a biophysical assay for inhalation toxicology

Cited by 3 publications

References 150 publications

Lung surfactant inhibition and cytotoxicity at the air-liquid interface of dry particle aerosols

Lung surfactant inhibition and cytotoxicity at the air-liquid interface of dry particle aerosols

High-throughput screening of respiratory hazards: Exploring lung surfactant inhibition with 20 benchmark chemicals

Evaluation of Machine Learning Based QSAR Models for the Classification of Lung Surfactant Inhibitors

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