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相关概念视频

Toxicity Testing in Animals01:23

Toxicity Testing in Animals

Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...

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相关实验视频

Updated: May 25, 2026

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
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多尺度信息嵌入式通用毒性预测框架

Lianlian Wu1,2, Fanmeng Wang3,4,5, Yixin Zhang2

  • 1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.

Environmental science & technology
|September 25, 2025
PubMed
概括
此摘要是机器生成的。

ToxScan是一种新的深度学习模型,通过分析3D结构和多个终点,准确地预测化学毒性. 这一框架改善了对罕见毒性和环境污染物的预测.

关键词:
深度学习是一种深度学习.环境污染物环境污染物毒性预测 毒性预测变压器的变压器是一个变压器.

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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
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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 机器学习

背景情况:

  • 准确的化学危险识别对于环境和健康安全至关重要.
  • 目前的深度学习模型在一般化方面扎,特别是在罕见的毒性和3D化学性质方面.
  • 现有的方法往往无法捕捉不同毒性终点之间的相互依赖.

研究的目的:

  • 开发一个通用的毒性预测框架,ToxScan,解决当前模型的局限性.
  • 将3D结构和立体化学信息纳入用于增强毒性概况.
  • 提高对各种毒性和环境污染物的概括性和准确性.

主要方法:

  • 拟议的ToxScan,一个包含3D几何的SE(3) -等价的多尺度模型.
  • 使用了双层分子和原子表示学习协议.
  • 实施了对通用毒理学特征的并行多尺度建模和多任务学习.

主要成果:

  • 毒素扫描显示,在各种毒性终点上,与最新的模型相比,7.8-37.6%的改善.
  • 该模型成功地区分了具有对比毒性的结构相似物.
  • 保持了环境污染物的概括性,并提供了可解释的原子级洞察力.

结论:

  • 毒素扫描为准确和可概括的化学毒性预测提供了一个强大的框架.
  • 该模型的可解释性有助于识别结构性警报和阐明污染物机制.
  • 一个可访问的网络平台可用于快速预测新化合物的毒性.