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相关概念视频

Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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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...
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Mutagenicity and Carcinogenicity01:25

Mutagenicity and Carcinogenicity

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Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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...
64
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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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...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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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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探索深度学习驱动的变异性QSAR模型的维度减少技术.

Alexander D Kalian1, Emilio Benfenati2, Olivia J Osborne3

  • 1Department of Nutritional Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford St., London SE1 9NH, UK.

Toxics
|July 28, 2023
PubMed
概括

像PCA这样的简单线性维度缩小方法对于深度学习QSAR模型是有效的. 非线性技术也对复杂的数据集有希望,改善毒理学预测.

关键词:
在QSAR中使用QSAR.自动编码器自动编码器化学信息学 化学信息学深度学习是一种深度学习.减少维度,减少维度.网格搜索 网格搜索 网格搜索超参数优化超参数优化在本地线性嵌入.突变性 突变性 突变性主要组件分析的主要组件分析

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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:

  • 深度学习定量结构-活性关系 (QSAR) 模型需要对高维的毒理学数据进行维度缩小.
  • 在QSAR中选择缩小维度的技术往往是任意的,缺乏彻底的研究.

研究的目的:

  • 为了比较六个维度减小技术 (线性和非线性) 在增强深度学习的QSAR模型对突变性预测的有效性.
  • 评估这些技术对模型性能和化学空间导航的影响.

主要方法:

  • 应用六维降低技术 (例如,PCA,内核PCA,自编码器) 对一个高维的致变性数据集.
  • 使用每个技术训练了一个深度学习的QSAR模型,通过网格搜索优化超参数.
  • 分析化学空间使用XLogP和分子量来定义适用性领域.

主要成果:

  • 主要组件分析 (PCA) 是一种线性技术,实现了QSAR模型的最佳性能,表明数据的近似线性可分离性.
  • 像内核PCA和自动编码器这样的非线性方法的性能可比,为潜在的非线性数据集提供了更广泛的适用性.
  • 分析显示,大多数数据都在适用性领域内,特定地区对业绩产生负面影响.

结论:

  • 对于QSAR模型来说,当数据是线性可分的时,线性维度减小技术就足够了.
  • 非线性技术为复杂的数据集提供了有价值的替代方案,并可以促进化学空间的独特导航.
  • 了解适用性领域内的数据分布对于强大的QSAR模型开发至关重要.