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Automatic Quantitative Structure–Activity Relationship Modeling to Fill Data Gaps in High-Throughput Screening

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High-Throughput Screening Assays in Toxicology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2474))

Abstract

Advances in high-throughput screening (HTS) revolutionized the environmental and health sciences data landscape. However, new compounds still need to be experimentally synthesized and tested to obtain HTS data, which will still be costly and time-consuming when a large set of new compounds need to be studied against many tests. Quantitative structure–activity relationship (QSAR) modeling is a standard method to fill data gaps for new compounds. The major challenge for many toxicologists, especially those with limited computational backgrounds, is efficiently develo** optimized QSAR models for each assay with missing data for certain test compounds. This chapter aims to introduce a freely available and user-friendly QSAR modeling workflow, which trains and optimizes models using five algorithms without the need for a programming background.

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Authors and Affiliations

  1. Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA

    Heather L. Ciallella, Elena Chung, Daniel P. Russo & Hao Zhu

  2. Department of Chemistry, Rutgers University, Camden, NJ, USA

    Daniel P. Russo & Hao Zhu

Authors
  1. Heather L. Ciallella
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  2. Elena Chung
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  3. Daniel P. Russo
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  4. Hao Zhu
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Corresponding author

Correspondence to Hao Zhu .

Editor information

Editors and Affiliations

  1. Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA

    Hao Zhu

  2. National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA

    Menghang **a

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Ciallella, H.L., Chung, E., Russo, D.P., Zhu, H. (2022). Automatic Quantitative Structure–Activity Relationship Modeling to Fill Data Gaps in High-Throughput Screening. In: Zhu, H., **a, M. (eds) High-Throughput Screening Assays in Toxicology. Methods in Molecular Biology, vol 2474. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2213-1_16

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  • DOI: https://doi.org/10.1007/978-1-0716-2213-1_16

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2212-4

  • Online ISBN: 978-1-0716-2213-1

  • eBook Packages: Springer Protocols

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