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相关概念视频

Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

49
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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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...
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Epistasis01:39

Epistasis

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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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The Two-State Receptor Model01:29

The Two-State Receptor Model

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The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
1.8K
Two-Compartment Open Model: Extravascular Administration01:12

Two-Compartment Open Model: Extravascular Administration

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The two-compartment model for extravascular administration represents a drug's absorption and distribution process. It features a central compartment, where the drug is first absorbed, and a peripheral compartment, which illustrates the drug's distribution throughout the body. The rate of change in drug concentration in the central compartment is calculated by three exponents: absorption, distribution, and elimination.
The absorption exponent (ka) indicates the speed at which the drug...
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相关实验视频

Updated: May 16, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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可解释的机器学习用于ETR和药物化物.

Edward Price1, Matthieu Dagommer1, Mattson Thieme1

  • 1Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, Illinois 60064, United States.

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概括

可解释的人工智能识别了关键的分子区域,用于设计超越利宾斯基五项规则的口服吸收药物. 这种方法加速了具有增强生物可用性的复杂分子的发展.

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科学领域:

  • 计算化学的计算化学
  • 药物发现 药物发现 药物发现
  • 机器学习 机器学习

背景情况:

  • 药物设计的传统in silico方法是计算密集的,不能完全捕捉超出五项规则 (bRo5) 药物的行为.
  • 设计口服生物可用bRo5药物需要了解和优化物理化学性质,特别是极性.

研究的目的:

  • 开发一种可解释的机器学习模型,用于识别影响bRo5药物中肉的分子"热点".
  • 为指导快速化学设计,以改善复杂的bRo5候选药物的口服吸收.

主要方法:

  • 引入EPSA与TPSA比率 (ETR) 作为极性降低的高通量度量.
  • 开发一种可解释的深度学习模型,使用大量bRo5分子 (宏循环,PROTACs) 的数据集.
  • 使用分子动力学模拟的模型见解的验证.

主要成果:

  • 可解释的深度学习模型准确地预测EPSA,并识别了影响药物混沌的极性降低"热点".
  • EPSA与TPSA比率 (ETR) 为评估bRo5化合物的极性降低提供了一种高通量测量方法.
  • 模型预测得到了分子动力学的验证,使得bRo5色龙行为的强大,高通量评估成为可能.

结论:

  • 可解释的人工智能可以有效地指导化学修改,以优化合成前bRo5药物的物理化学特性.
  • 这种方法为设计具有改善口服生物利用性的复杂bRo5药物建立了新的框架,基于Lipinski规则等现有描述符.