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相关概念视频

Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower Kd...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

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相关实验视频

Updated: Jun 29, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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可解释多模式共识 QSAR框架:集成机器和深度学习,以加强多终点毒性评估.

Fauzan Syarif Nursyafi1, Muhammad Adnan Pramudito2, Yunendah Nur Fuadah3

  • 1Computational Medicine Lab, Department of Biomedical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.

Toxicology mechanisms and methods
|March 17, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个新的计算框架,用于在八个终点上预测化学毒性. 多模式共识定量结构-活性关系 (QSAR) 模型为化学安全评估提供了更高的准确性和可靠性.

关键词:
多个终点毒性预测多个终点毒性预测多模式共识共识多模式共识在QSAR中使用QSAR.这就是SHAP XAI.机器学习和深度学习.

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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
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相关实验视频

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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
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科学领域:

  • 计算毒理学计算毒理学
  • 化学信息学 化学信息学
  • 预测建模预测建模

背景情况:

  • 实验性毒性测试是昂贵和耗时的.
  • 现有的定量结构-活动关系 (QSAR) 模型由于使用单一描述符/算法和有限的数据集,往往缺乏稳定性.
  • 需要更全面,更可靠的化学安全评估计算方法.

研究的目的:

  • 开发一个可解释的多模式共识QSAR框架.
  • 预测八种不同的毒性终点 (皮肤敏感,呼吸系统毒性,AMES致变性,肝毒性,发育毒性,心脏毒性,药物诱导的毒性,神经毒性).
  • 将多个分子表示与机器学习和深度学习集成在一起,以提高预测.

主要方法:

  • 开发了一个共识QSAR框架,整合了各种分子表示.
  • 采用机器学习和深度学习算法.
  • 使用10倍交叉验证和基于AUC的加权共识预测的优化模型.
  • 在未见的和外部数据集上评估性能.

主要成果:

  • 在所有终点上,多模式共识模型实现了中等至优异的性能 (AUC 0.80-0.99,BACC 0.76-0.90).
  • 在8个终点中的7个中,共识模型的表现明显优于个别模型 (p < 0.05).
  • 适用性领域和SHAP分析支持模型可靠性和生物可信性.

结论:

  • 开发的多模式共识框架为广泛的毒性预测提供了可靠和可解释的方法.
  • 这种方法通过在多个毒性终点上提供准确的预测来提高化学安全评估.
  • 该框架证明了各种化学化合物的广泛适用性和强大的性能.