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相关概念视频

Classification of Neurotransmitters01:30

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Bioavailability Enhancement: Determination and Conceptual Approaches in Overcoming Bioavailability Problems01:22

Bioavailability Enhancement: Determination and Conceptual Approaches in Overcoming Bioavailability Problems

Bioavailability is a critical pharmacological concept that measures the extent and rate at which an active drug ingredient or therapeutic moiety enters the systemic circulation, remaining unchanged. It's a pivotal factor in determining a drug's efficacy and safety.The Biopharmaceutics Classification System (BCS) plays an essential role in drug development by categorizing drugs into four classes based on their solubility and permeability. This classification aids in understanding drug absorption...
Modified-Release Drug Delivery Systems: Classification01:23

Modified-Release Drug Delivery Systems: Classification

Modified-release drug delivery systems improve drug efficacy and minimize side effects by controlling the rate and location of drug release. These systems fall into three categories: rate-programmed, stimuli-activated, and site-targeted.Rate-programmed systems release drugs at a predetermined rate, maintaining consistent therapeutic levels and reducing fluctuations that could lead to toxicity or subtherapeutic effects. These systems use polymeric matrices, reservoir-based designs, or osmotic...

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相关实验视频

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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配方BCS:基于生物制药分类系统 (BCS) 类预测的多种分子表示的机器学习平台.

Zheng Wu1, Nannan Wang1, Zhuyifan Ye2

  • 1Institute of Chinese Medical Sciences (ICMS), State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macau 999078, China.

Molecular pharmaceutics
|December 8, 2024
PubMed
概括

这项研究开发了一个机器学习网络平台,用于高通量生物制药分类系统 (BCS) 的分类. 它使药物溶解性和透性的快速in silico评估成为可能,提高了药物开发效率.

关键词:
在BCS预测预测.人工智能平台的人工智能平台机器学习是机器学习.透性 透性的预制的 预制的 预制溶解度 溶解度 溶解度 溶解度

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科学领域:

  • 计算化学和化学信息学
  • 药理动力学和药物代谢的药理动力学
  • 机器学习在药物发现中的作用

背景情况:

  • 生物制药分类系统 (BCS) 对药物生物豁免者至关重要,提高了监管效率.
  • 目前用于测量BCS特性 (溶解度,透性) 的方法限制了高通量候选药物的评估.
  • 机器学习 (ML) 和定量结构-性能关系 (QSPR) 提供了快速的基BCS分类的潜力.

研究的目的:

  • 使用先进的ML模型开发一个用于高通量BCS分类的Web平台.
  • 为了使药物溶解性和透性的快速和准确的in silico预测.
  • 通过促进BCS评估和指导配方决策,支持早期药物开发.

主要方法:

  • 策划了四个数据集,用于BCS相关的分子特性:log S,log P,log D和log P_app.
  • 采用了6个ML算法和深度学习框架,具有多种分子表示 (指纹,描述符,图形,3D坐标).
  • 开发并验证了ML模型来预测可溶性,透性 (log P,log D) 和表面透性 (log P_app).

主要成果:

  • 对于溶解度预测,LightGBM实现了R2=0.84.
  • 注意FP 显示了 log P 的 R2=0.96 和 log D (通透性) 的 R2=0.76.
  • 对于日志P_app预测,XGBoost产生了R2=0.71;外部验证显示分别可溶性和透性的准确度>77%和>73%.

结论:

  • 开发的ML模型准确地预测了与BCS相关的特性.
  • 创建了第一个基于ML的BCS类预测网络平台 (xf).
  • 这个平台促进了高通量BCS评估,降低了风险并提高了药物开发效率.