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相关概念视频

Drug Discovery: Overview01:26

Drug Discovery: Overview

7.7K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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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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Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

687
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...
687
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
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相关实验视频

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High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
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开发一种使用基于PU学习的数据增强策略的半监督方法,用于多目标药物发现.

Yang Hao1,2, Bo Li1,2, Daiyun Huang1,3

  • 1Wisdom Lake Academy of Pharmacy, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.

International journal of molecular sciences
|August 10, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了Negative-Augmented PU-bagging (NAPU-bagging) SVM,这是一种用于药物发现的新型机器学习方法. 这种方法通过提高准确性和召回率来增强对多目标导向联体 (MTDL) 的虚拟选.

关键词:
这是PU学习.支持矢量机器 (SVM) 是一个支持矢量机器.多目标药物 药物多目标药物虚拟选 虚拟选 虚拟选

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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习在药物发现中的作用
  • 药品化学 药品化学 是一个

背景情况:

  • 多因素性疾病需要多目标疗法,但它们的临床批准有限.
  • 机器学习 (ML) 和深度学习 (DL) 在药物发现中推进了虚拟选.
  • 对于ML/DL模型的现有数据增强方法往往会在真正和假正比率之间达成妥协.

研究的目的:

  • 调查ML/DL方法,分子表示和数据增强对药物发现的综合影响.
  • 引入一个新的半监督学习框架,负增强的PU包装 (NAPU包装) SVM,以解决数据增强的局限性.
  • 应用开发的框架来识别多目标导向连接体 (MTDL).

主要方法:

  • 评估了支持矢量机器 (SVM) 与最先进的DL方法的性能.
  • 开发并实施了NAPU支持的SVM,这是一个使用集体SVM的半监督学习框架.
  • 训练有素的分类器在重新采样的袋子上包含积极的,消极的和未标记的数据,以管理错误的阳性率,同时保持高回忆率.
  • 应用了NAPU支持的SVM来识别MTDL,专注于候选化合物列表的高回忆率.

主要成果:

  • 在某些情况下,SVM的性能与先进的DL方法相美或优于其.
  • 随着NAPU的落后,SVM有效地管理了虚假阳性率,同时实现了高回忆率.
  • 该方法确定了ALK-EGFR和多巴胺受体的新型MTDL候选者,具有有前途的对接分数和结合模式.
  • 案例研究证实了NAPU-backed SVM在识别结构新型MTDL方面的实用性.

结论:

  • 支持NAPU的SVM框架为药物发现中的半监督学习提供了一个强大的方法.
  • 这种方法对虚拟选有很大的前景,特别是在具有挑战性的MTDL发现领域.
  • 开发的技术可以提高识别复杂疾病的新疗法候选者的效率和成功率.