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In Silico Prediction of Oral Acute Rodent Toxicity Using Consensus Machine Learning
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    In Silico Prediction of Oral Acute Rodent Toxicity Using Consensus Machine Learning
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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2024, 64, 8, 3114–3122
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    https://doi.org/10.1021/acs.jcim.4c00056
    Published March 18, 2024
    Copyright © 2024 The Authors. Published by American Chemical Society

    Abstract

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    Acute oral toxicity (AOT) is required for the classification and labeling of chemicals according to the global harmonized system (GHS). Acute oral toxicity studies are optimized to minimize the use of animals. However, with the advent of the three Rs principles and machine learning in toxicology, alternative in silico methods became a reasonable alternative approach for addressing the AOT of new chemical matter. Here, we describe the compilation of AOT data from a commercial database and the development of a consensus classification model after evaluating different combinations of molecular representations and machine learning algorithms. The model shows significantly better performance compared to publicly available AOT models. Its performance was evaluated on an external validation data set, which was compiled from the literature, and an applicability domain was deduced.

    Copyright © 2024 The Authors. Published by American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.4c00056.

    • Compound structures (SMILES) and GHS categories of the validation data set (XLSX)

    • Details about molecular structure curation, calculation of molecular descriptors and fingerprints, data set splitting, t-SNE calculation, SOM calculation, calculation of SALI and MODI, machine learning, figures describing the training data set descriptor distribution, Tanimoto-distance matrix, example compounds from the training data set with different modes of action for AOT and pairwise SALI map for each compound of the training data set, table of GHS categories and corresponding LD50 bins and compound counts for each GHS category in the training data set, List of used 2D descriptors, list of used 3D descriptors, featurization of GNNs, individual model performance, and y-scrambling results (PDF)

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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

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    This article is cited by 2 publications.

    1. Yunendah Nur Fuadah, Muhammad Adnan Pramudito, Lulu Firdaus, Frederique J. Vanheusden, Ki Moo Lim. QSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End Points. ACS Omega 2024, 9 (51) , 50796-50808. https://doi.org/10.1021/acsomega.4c09356
    2. Dmitry S. Boichenko, Nikita I. Kolomoets, Daniil A. Boiko, Alexey S. Galushko, Alexandra V. Posvyatenko, Andrey E. Kolesnikov, Ksenia S. Egorova, Valentine P. Ananikov. Build-a-Bio-Strip: An Online Platform for Rapid Toxicity Assessment in Chemical Synthesis. Journal of Chemical Information and Modeling 2024, 64 (22) , 8373-8378. https://doi.org/10.1021/acs.jcim.4c01381

    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2024, 64, 8, 3114–3122
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jcim.4c00056
    Published March 18, 2024
    Copyright © 2024 The Authors. Published by American Chemical Society

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