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

Liu, James Y.; Peeples, Joshua; Sayes, Christie M.

doi:10.1021/envhealth.4c00118

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

Journal Article · 20 September 2024 · Environment & Health

DOI: https://doi.org/10.1021/envhealth.4c00118 · OSTI ID:2447521

^[1]; Peeples, Joshua ^[2];

^[1]

Department of Environmental Science, Baylor University, Waco, Texas 76798-7266, United States
Department of Electrical & Computer Engineering, Texas A&M University, College Station, Texas 77845, United States

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Sponsoring Organization:: USDOE

OSTI ID:: 2447521

Journal Information:: Environment & Health, Journal Name: Environment & Health Journal Issue: 12 Vol. 2; ISSN 2833-8278

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: Country unknown/Code not available

Language:: English

References (12)

Best Practices for QSAR Model Development, Validation, and Exploitation Tropsha, Alexander Molecular Informatics, Vol. 29, Issue 6-7 https://doi.org/10.1002/minf.201000061	journal	July 2010
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery Olier, Ivan; Sadawi, Noureddin; Bickerton, G. Richard Machine Learning, Vol. 107, Issue 1 https://doi.org/10.1007/s10994-017-5685-x	journal	December 2017
Novel local (fragment-based) topological molecular descriptors for QSPR/QSAR and molecular design Estrada, Ernesto; Molina, Enrique Journal of Molecular Graphics and Modelling, Vol. 20, Issue 1 https://doi.org/10.1016/S1093-3263(01)00100-0	journal	December 2001
Composition, structure and mechanical properties define performance of pulmonary surfactant membranes and films Parra, Elisa; Pérez-Gil, Jesús Chemistry and Physics of Lipids, Vol. 185 https://doi.org/10.1016/j.chemphyslip.2014.09.002	journal	January 2015
Risk assessment of consumer spray products using in vitro lung surfactant function inhibition, exposure modelling and chemical analysis Sørli, J. B.; Sengupta, S.; Jensen, A. C. Ø. Food and Chemical Toxicology, Vol. 164 https://doi.org/10.1016/j.fct.2022.112999	journal	June 2022
High-throughput screening of respiratory hazards: Exploring lung surfactant inhibition with 20 benchmark chemicals Liu, James Y.; George, Ian C.; Hussain, Saber Toxicology, Vol. 504 https://doi.org/10.1016/j.tox.2024.153785	journal	May 2024
A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition Novotarskyi, Sergii; Sushko, Iurii; Körner, Robert Journal of Chemical Information and Modeling, Vol. 51, Issue 6 https://doi.org/10.1021/ci200091h	journal	May 2011
XGBoost: A Scalable Tree Boosting System Chen, Tianqi; Guestrin, Carlos Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 https://doi.org/10.1145/2939672.2939785	conference	January 2016
Mordred: a molecular descriptor calculator Moriwaki, Hirotomo; Tian, Yu-Shi; Kawashita, Norihito Journal of Cheminformatics, Vol. 10, Issue 1 https://doi.org/10.1186/s13321-018-0258-y	journal	February 2018
QSAR: Then and Now Selassie, C.; Mekapati, S.; Verma, R. Current Topics in Medicinal Chemistry, Vol. 2, Issue 12 https://doi.org/10.2174/1568026023392823	journal	December 2002
Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer Ozcan, Irem; Aydin, Hakan; Cetinkaya, Ali Asian Pacific Journal of Cancer Prevention, Vol. 23, Issue 10 https://doi.org/10.31557/APJCP.2022.23.10.3287	journal	October 2022
Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods Myint, Kyaw Zeyar; Xie, Xiang-Qun International Journal of Molecular Sciences, Vol. 11, Issue 10 https://doi.org/10.3390/ijms11103846	journal	October 2010

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