Evaluation of Machine Learning Based QSAR Models for the Classification of Lung Surfactant Inhibitors Published as part of Environment&Health special issue“Artificial Intelligence and Machine Learning for Environmental Health”  

作　　者：James Y.Liu Joshua Peeples Christie M.Sayes 

机构地区：[1]Department of Environmental Science,Baylor University,Waco,Texas 76798-7266,United States [2]Department of Electrical&Computer Engineering,Texas A&M University,College Station,Texas 77845,United States

出　　处：《Environment & Health》2024年第12期912-917,共6页环境与健康(英文)

基　　金：J.Y.L.received student internship funds from the Oak Ridge Institute for Science and Education(ORISE)through an interagency agreement between the U.S.Department of Energy and the Air Force Research Laboratory(AFRL,AFRL-2023-4907);J.Y.L.and C.M.S.thank the Baylor Department of Environmental Science and the Henry F Jackson Foundation for financially supporting this research.

摘　　要：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.

关 键 词：machine learning QSAR lung surfactant hazard screening 

分 类 号：O64[理学—物理化学]