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A Distance-Based Boolean Applicability Domain for Classification of High Throughput Screening Data

  • Francois Berenger*
    Francois Berenger
    Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Japan
    *E-mail: [email protected]
  •  and 
  • Yoshihiro Yamanishi
    Yoshihiro Yamanishi
    Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Japan
    PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan
Cite this: J. Chem. Inf. Model. 2019, 59, 1, 463–476
Publication Date (Web):December 19, 2018
https://doi.org/10.1021/acs.jcim.8b00499
Copyright © 2018 American Chemical Society

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    Abstract

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    In Quantitative Structure–Activity Relationship (QSAR) modeling, one must come up with an activity model but also with an applicability domain for that model. Some existing methods to create an applicability domain are complex, hard to implement, and/or difficult to interpret. Also, they often require the user to select a threshold value, or they embed an empirical constant. In this work, we propose a trivial to interpret and fully automatic Distance-Based Boolean Applicability Domain (DBBAD) algorithm for category QSAR. In retrospective experiments on High Throughput Screening data sets, this applicability domain improves the classification performance and early retrieval of support vector machine and random forest based classifiers, while improving the scaffold diversity among top-ranked active molecules.

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    Cited By

    This article is cited by 12 publications.

    1. Francois Berenger, Yoshihiro Yamanishi. Ranking Molecules with Vanishing Kernels and a Single Parameter: Active Applicability Domain Included. Journal of Chemical Information and Modeling 2020, 60 (9) , 4376-4387. https://doi.org/10.1021/acs.jcim.9b01075
    2. Isidro Cortés-Ciriano, Andreas Bender. Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout. Journal of Chemical Information and Modeling 2019, 59 (7) , 3330-3339. https://doi.org/10.1021/acs.jcim.9b00297
    3. Yuriko Okazaki, Shinichiro Okazaki, Shingo Asamoto, Toru Yamaji, Minoru Ishige. Estimator for generalization performance of machine learning model trained by biased data collected from multiple references. Computer-Aided Civil and Infrastructure Engineering 2023, 38 (15) , 2145-2162. https://doi.org/10.1111/mice.12992
    4. Min Han, Biao Jin, Jun Liang, Chen Huang, Hans Peter H. Arp. Developing machine learning approaches to identify candidate persistent, mobile and toxic (PMT) and very persistent and very mobile (vPvM) substances based on molecular structure. Water Research 2023, 244 , 120470. https://doi.org/10.1016/j.watres.2023.120470
    5. Luiz Henrique Dias de Oliveira, Jorddy Neves Cruz, Cleydson Breno Rodrigues dos Santos, Eduardo Borges de Melo. Multivariate QSAR, similarity search and ADMET studies based in a set of methylamine derivatives described as dopamine transporter inhibitors. Molecular Diversity 2023, 19 https://doi.org/10.1007/s11030-023-10724-5
    6. Vadim Korolev, Iurii Nevolin, Pavel Protsenko. A universal similarity based approach for predictive uncertainty quantification in materials science. Scientific Reports 2022, 12 (1) https://doi.org/10.1038/s41598-022-19205-5
    7. Jing Li, Chuanxi Wang, Le Yue, Feiran Chen, Xuesong Cao, Zhenyu Wang. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review. Ecotoxicology and Environmental Safety 2022, 243 , 113955. https://doi.org/10.1016/j.ecoenv.2022.113955
    8. Jie Yu, Dingyan Wang, Mingyue Zheng. Uncertainty quantification: Can we trust artificial intelligence in drug discovery?. iScience 2022, 25 (8) , 104814. https://doi.org/10.1016/j.isci.2022.104814
    9. Dingyan Wang, Jie Yu, Lifan Chen, Xutong Li, Hualiang Jiang, Kaixian Chen, Mingyue Zheng, Xiaomin Luo. A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling. Journal of Cheminformatics 2021, 13 (1) https://doi.org/10.1186/s13321-021-00551-x
    10. Robert Ancuceanu, Marilena Viorica Hovanet, Adriana Iuliana Anghel, Florentina Furtunescu, Monica Neagu, Carolina Constantin, Mihaela Dinu. Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset. International Journal of Molecular Sciences 2020, 21 (6) , 2114. https://doi.org/10.3390/ijms21062114
    11. Robert Ancuceanu, Bogdan Tamba, Cristina Silvia Stoicescu, Mihaela Dinu. Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase. International Journal of Molecular Sciences 2020, 21 (1) , 19. https://doi.org/10.3390/ijms21010019
    12. María Virginia Sabando, Ignacio Ponzoni, Axel J. Soto. Neural-based approaches to overcome feature selection and applicability domain in drug-related property prediction. Applied Soft Computing 2019, 85 , 105777. https://doi.org/10.1016/j.asoc.2019.105777

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