Use of Machine Learning and Classical QSAR Methods in Computational Ecotoxicology

  • Renata P. C. Barros
  • Natália F. Sousa
  • Luciana Scotti
  • Marcus T. Scotti
Part of the Methods in Pharmacology and Toxicology book series (MIPT)


In recent years, there has been an increase in concern about environmental pollution and human health, especially in the areas of manufacturing, storage, distribution, and release of hazardous substances after use. Several researchers have been dedicating studies to develop methods to identify and assess the toxicity of chemicals. Quantitative structure-activity relationship (QSAR) modeling has evolved a lot in recent years and also developed in the area of ecotoxicology. In the course of this evolution, there was the application of machine learning techniques in QSAR studies. The use of ML algorithms is a great approach for assessing toxicity to generate predictive models involving QSAR. Several studies are being conducted not only comparing ML techniques but applying them to generate potentially predictive models and excellent performances.

Key words

Ecotoxicology Machine learning Quantitative structure-activity relationship (QSAR) 




Brazilian Association of Technical Standards


Environmental Company of the State of São Paulo


Comma-Separated Values


Decision tree


European Centre for Ecotoxicology and Toxicology of Chemicals


European Chemicals Agency


Ecological structure-activity relationships predictive model


Effect concentration 50%


Ensemble learning


Lethal concentration 50%


K-nearest neighbor


Machine learning


Multiple linear regression


The toxic mode of action


Neural networks


Online Chemical Database


The Organisation for Economic Co-operation and Development


Quantitative structure-activity relationship


Quantitative structure-property relationship (QSPR)


Regulation on Registration, Evaluation, Authorisation and Restriction of Chemicals


Random forest


Structure-activity relationships


Structure Data File


Support vector machine


US Environmental Protection Agency


Toxicity unit


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© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Renata P. C. Barros
    • 1
  • Natália F. Sousa
    • 1
  • Luciana Scotti
    • 1
  • Marcus T. Scotti
    • 1
  1. 1.Laboratory of Chemoinformatics, Postgraduate Program in Natural and Synthetic Bioactive ProductsFederal University of ParaíbaJoão PessoaBrazil

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