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Machine Learning and Deep Learning Methods in Ecotoxicological QSAR Modeling

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Ecotoxicological QSARs

Part of the book series: Methods in Pharmacology and Toxicology ((MIPT))

Abstract

Today the registered chemical structures are about 28 millions, while experimental toxicity data are available for a few hundred thousands of them. Defining properties and effects for all the available chemicals is a huge task due to the cost of the experimentation and to legislative restrictions. Therefore, prediction is the only available solution, but it poses many challenges in terms of accuracy and interpretability. Predictive toxicology systems use statistics as well as methods based on machine learning (ML). While ML has been widely used in the pharmaceutical domain, its use in ecotoxicology is more limited. After reviewing the experiences in quantitative structure-activity relationships (QSARs) for modeling CMR (carcinogenic, mutagenic, reproductive) toxicity and PBT (persistent, bioaccumulative, and toxic) chemicals, we look at the advancements of technology in ML. Recently, the investigation of the neural basis for many cognitive functions has provided the tools to create new systems that can think, solve problems, find patterns, and recognize images and texts; these new methods are named deep learning (DL). We modified the most successful DL architecture, implemented Toxception as a tool to generate QSAR models, and tested it in a real case, on a dataset of about 20,000 molecules tested for mutagenicity with the Ames test. The results obtained challenge the current state of the art. In addition, Toxception does not use any chemistry knowledge besides the 2D structures derived from SMILES. We conclude examining advantages, open challenges, and drawbacks of building QSARs with DL.

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Notes

  1. 1.

    https://www.nvidia.com/en-us/data-center/dgx-1/

  2. 2.

    https://www.vegahub.eu/download/

  3. 3.

    https://toxnet.nlm.nih.gov/cpdb/

  4. 4.

    ToxTree: http://toxtree.sourceforge.net/

  5. 5.

    https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test

  6. 6.

    http://image-net.org/challenges/LSVRC/

  7. 7.

    https://www.kaggle.com/c/MerckActivity

  8. 8.

    Rdkit. URL https://bit.ly/2OYLjj9

  9. 9.

    Talos. URL https://bit.ly/2yL9gQJ

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Authors and Affiliations

  1. DEIB, Politecnico di Milano, Milan, MI, Italy

    Giuseppina Gini & Francesco Zanoli

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  1. Giuseppina Gini
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  2. Francesco Zanoli
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Correspondence to Giuseppina Gini .

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  1. Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India

    Kunal Roy

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Gini, G., Zanoli, F. (2020). Machine Learning and Deep Learning Methods in Ecotoxicological QSAR Modeling. In: Roy, K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0150-1_6

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  • DOI: https://doi.org/10.1007/978-1-0716-0150-1_6

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