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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

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Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
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Nonlinear Pharmacokinetics: Bioavailability and Protein-Drug Binding01:22

Nonlinear Pharmacokinetics: Bioavailability and Protein-Drug Binding

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When a drug follows nonlinear pharmacokinetics, its bioavailability, the amount of the drug that reaches the systemic circulation, can change with different doses. This is due to the presence of a saturable pathway. The pathway becomes saturated as the drug concentration increases, decreasing the absorption rate. Consequently, the drug's bioavailability may be lower than expected at higher doses.
To quantify the extent of bioavailability, pharmacologists often use a parameter called .
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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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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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通过基于物理的几次射击学习改进了药物蛋白相互作用的预测.

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通过准确预测药物与蛋白质的相互作用,即便数据有限,DrugBaiter也改善了药物发现. 这种可解释的机器学习模型增强了针对新目标的药物查.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 机器学习 机器学习

背景情况:

  • 准确预测药物蛋白相互作用对于有效的药物发现至关重要.
  • 传统的评分函数和现有的机器学习评分函数 (MLSFs) 面临着局限性,包括小数据问题和糟糕的解释性.
  • 基于结构的药物查需要强大的预测模型.

研究的目的:

  • 开发一种新的基于物理的小型数据机器学习框架,用于可解释和可概括的药物蛋白相互作用预测.
  • 解决现有MLSF中数据有限和解释性差的挑战.
  • 为药物查提供一个工具,对目标提供稀缺的积极数据.

主要方法:

  • 提出了DrugBaiter,一个基于物理的小型数据机器学习框架.
  • 采用了三阶段训练的策略,有三个损失函数 (分数,重量和排名).
  • 在DUD-E (102个目标) 和DEKOIS 2.0 (81个目标) 上评估DrugBaiter,将其与其他14个MLSF进行比较.

主要成果:

  • 药物查器显著提高了药物查性能,特别是对于已知活性化合物很少的目标.
  • 该模型证明了在描述原子水平上药物蛋白相互作用的可解释性.
  • 成功应用DrugBaiter用于选SARS-CoV-2主要蛋白酶标药物.

结论:

  • 药物贝特为可解释和可概括的药物蛋白相互作用预测提供了强大的解决方案,特别是在数据不足的情况下.
  • 该框架提高了药物查效率,并为相互作用提供了原子级的洞察力.
  • 药物探器准备成为一种有价值的工具,用于选新药与有限的活性数据的新目标.