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Use of Machine Learning and Classical QSAR Methods in Computational Ecotoxicology

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

Abstract

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) 

Notes

Glossary

ABNT

Brazilian Association of Technical Standards

CETESB

Environmental Company of the State of São Paulo

CSV

Comma-Separated Values

DT

Decision tree

ECETOC

European Centre for Ecotoxicology and Toxicology of Chemicals

ECHA

European Chemicals Agency

ECOSAR

Ecological structure-activity relationships predictive model

EC50

Effect concentration 50%

EL

Ensemble learning

EL50

Lethal concentration 50%

K-NN

K-nearest neighbor

ML

Machine learning

MLR

Multiple linear regression

MOA

The toxic mode of action

NN

Neural networks

OCHEM

Online Chemical Database

OECD

The Organisation for Economic Co-operation and Development

QSAR

Quantitative structure-activity relationship

QSPR

Quantitative structure-property relationship (QSPR)

REACH

Regulation on Registration, Evaluation, Authorisation and Restriction of Chemicals

RF

Random forest

SARs

Structure-activity relationships

SDF

Structure Data File

SVM

Support vector machine

US EPA

US Environmental Protection Agency

UT

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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