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Related Concept Videos

Toxic Reactions: Overview01:26

Toxic Reactions: Overview

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When toxic substances penetrate the human body, they disseminate to various tissues, undergoing metabolic changes. This process yields reactive metabolites that may covalently bind with specific target molecules, resulting in toxicity.
Toxicity falls into two primary categories: local and systemic.
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Humans continually engage with an environment rich in potentially harmful chemicals. These are introduced to our bodies through inhalation, ingestion, or skin contact. These chemicals exist in various forms, such as air and environmental pollutants, agricultural chemicals, organic solvents, and heavy metals.
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Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for

Lama Moukheiber1, William Mangione2, Mira Moukheiber2

  • 1Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY 14260, USA.

Molecules (Basel, Switzerland)
|May 14, 2022
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Summary
This summary is machine-generated.

This study uses machine learning to identify key protein features linked to compound toxicity, revealing upstream/downstream biological pathways involved in adverse health effects.

Keywords:
drug behaviorenrichment analysisfeature selectionhigh-throughput screeningmachine learningproteomic signaturerandom foreststructure–activity relationshipstoxicity

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Area of Science:

  • Computational toxicology and cheminformatics
  • Proteomics and systems biology
  • Machine learning applications in drug discovery

Background:

  • Chemicals pose health risks, necessitating robust toxicity prediction methods.
  • Machine learning models excel at predicting toxicity from chemical structures but overlook proteomic data.
  • Investigating toxicity-related protein features is crucial for a comprehensive understanding of compound effects.

Purpose of the Study:

  • To develop a computational pipeline for predicting critical protein features associated with compound toxicity.
  • To analyze toxicity data from the Tox21 dataset using machine learning and the CANDO platform.
  • To identify and elucidate biological pathways and protein interactions involved in toxicity.

Main Methods:

  • Utilized machine learning models, specifically random forest with SMOTE+ENN resampling, to handle imbalanced toxicity data.
  • Applied a computational pipeline within the CANDO platform to analyze the Tox21 dataset (10,000+ compounds, 12 assays).
  • Performed enrichment analysis on extracted protein features to identify implicated biological pathways and validated findings for the aryl hydrocarbon receptor (AhR).

Main Results:

  • Achieved high performance in predicting toxicity endpoints, with AUCROCs of 0.90 for NR-AhR and 0.92 for SR-MMP.
  • Identified key protein features and elucidated upstream/downstream biological pathways correlated with compound toxicity.
  • Demonstrated significant relationships between protein-compound interactions, computed pathways, and toxicity endpoints.

Conclusions:

  • The developed machine learning pipeline effectively predicts toxicity and identifies crucial proteomic features.
  • The study highlights interconnected biological pathways and protein interactions underlying compound toxicity.
  • This research advances the understanding of toxicity mechanisms at a proteomic level, aiding therapeutic discovery.