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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.
Local toxicity appears at the exposure site, such as protein denaturation caused by caustic substances.
In contrast, systemic toxicity requires the toxic agent's absorption and distribution,...
969
Types of Toxins01:36

Types of Toxins

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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.
Air pollutants, primarily gases, pose significant threats to respiratory health, leading to conditions like hypoxia, lung cancer, and in extreme cases, death.
Environmental pollutants like...
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Updated: Jun 27, 2025

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
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通过提取学习化学特征来解释神经网络模型进行毒性预测.

Moritz Walter1, Samuel J Webb2, Valerie J Gillet1

  • 1Information School, University of Sheffield, The Wave, 2 Whitham Road, Sheffield S10 2AH, U.K.

Journal of chemical information and modeling
|April 30, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法,通过识别激活隐藏神经元的化学基层来解释神经网络毒性预测. 这种方法增强了对复杂模型的理解,并补充了现有的特征归因技术.

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科学领域:

  • 计算化学是一种计算化学.
  • 毒理学 毒理学 毒理学
  • 机器学习是机器学习.

背景情况:

  • 神经网络被广泛用于化学毒性预测.
  • 它们的复杂性阻碍了解释性,限制了用户对预测的信心.
  • 像SHAP这样的现有解释方法专注于输入特征,而不是内部网络转换.

研究的目的:

  • 在化学毒理学中开发一种用于解释神经网络预测的新技术.
  • 识别特定的化学子结构,负责激活网络内的隐藏神经元.
  • 通过将隐藏的神经元活动与已学习的化学表征联系起来来解释模型预测.

主要方法:

  • 开发了一种新的解释方法,以识别与隐藏神经元激活相关的化学子结构.
  • 对于单个测试化合物来说,隐藏神经元的重要性被评估.
  • 与激活的神经元相关的子结构被用来解释预测.
  • 该方法与来自Derek Nexus系统的已知突变致病性结构警报进行了验证.

主要成果:

  • 这种新的技术成功地确定了负责隐藏神经元激活的化学子结构.
  • 从这种方法获得的解释被发现与已建立的特征归因技术具有竞争力.
  • 该方法为神经网络如何学习和预测毒性提供了互补的见解.

结论:

  • 开发的方法为解释毒理学中复杂的神经网络模型提供了一种强大的新方法.
  • 它通过揭示模型预测的化学基础来提高透明度.
  • 这种技术可以提高对基于机器学习的毒性评估的信心和信任.