Artificial neural networks in contemporary toxicology research

Igor Pantic, Jovana Paunovic, Jelena Cumic, Svetlana Valjarevic, Georg Petroianu, Peter R. Corridon

Research output: Contribution to journalReview articlepeer-review

29 Scopus citations

Abstract

Artificial neural networks (ANNs) have a huge potential in toxicology research. They may be used to predict toxicity of various chemical compounds or classify the compounds based on their toxic effects. Today, numerous ANN models have been developed, some of which may be used to detect and possibly explain complex chemico-biological interactions. Fully connected multilayer perceptrons may in some circumstances have high classification accuracy and discriminatory power in separating damaged from intact cells after exposure to a toxic substance. Regularized and not fully connected convolutional neural networks can detect and identify discrete changes in patterns of two-dimensional data associated with toxicity. Bayesian neural networks with weight marginalization sometimes may have better prediction performance when compared to traditional approaches. With the further development of artificial intelligence, it is expected that ANNs will in the future become important parts of various accurate and affordable biosensors for detection of various toxic substances and evaluation of their biochemical properties. In this concise review article, we discuss the recent research focused on the scientific value of ANNs in evaluation and prediction of toxicity of chemical compounds.

Original languageBritish English
Article number110269
JournalChemico-Biological Interactions
Volume369
DOIs
StatePublished - 5 Jan 2023

Keywords

  • Biochemistry
  • Convolutional neural network
  • Perceptron
  • Pharmacology
  • Toxicity

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