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相关概念视频

Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
Drug Discovery: Overview01:26

Drug Discovery: Overview

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...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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相关实验视频

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使用KNIME进行COX-2抑制剂预测:基于机器学习的无码自动化虚拟查工作流程.

Powsali Ghosh1, Ashok Kumar1, Sushil Kumar Singh1

  • 1Pharmaceutical Chemistry Research Laboratory 1, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.

Journal of computational chemistry
|January 11, 2025
PubMed
概括

这项研究提出了一个自动化的KNIME工作流来预测循环氧化酶-2 (COX-2) 抑制剂,这对于治疗癌症和阿尔茨海默病等与炎症相关的疾病至关重要. 这种可访问的工具不需要编码,可以快速发现药物.

关键词:
循环氧化原酶-2 抑制剂一种类型的机械.发现药物的发现.机器学习是机器学习.虚拟选是虚拟的选.

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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
  • 生物信息学是一种生物信息学.

背景情况:

  • 循环氧化酶-2 (COX-2) 的过度表达与癌症,关节炎和阿尔茨海默病 (AD) 等疾病的炎症有关.
  • 在 silico 虚拟查有助于药物发现,但需要机器学习专业知识来准确的预测模型.
  • 开发用户友好的工具对于扩大计算方法在识别潜在药物候选者的应用至关重要.

研究的目的:

  • 开发一种自动化的KNIME工作流程,用于预测新型分子的COX-2抑制潜力.
  • 使用多个机器学习算法和分子描述器构建一个强大的集合模型.
  • 为预测COX-2抑制剂提供可访问的工具,而不需要编码或机器学习知识.

主要方法:

  • 创建了一个自动化的KNIME工作流程,用于预测COX-2抑制剂.
  • 使用逻辑回归,K-最近邻居,决策树,随机森林和极端梯度增强算法构建了一个多层次的集合模型.
  • 他们使用了各种分子和指纹描述器 (AtomPair,Avalon,MACCS,Morgan,RDKit,Pattern).

主要成果:

  • 最终的组合模型在适用性域过后在外部验证集上实现了90.0%的平衡精度,87.7%的精度和86.4%的回忆.
  • 该工作流显示了COX-2抑制剂化合物的高预测性能.
  • 开发的模型有效地整合了各种机器学习算法和分子描述器.

结论:

  • 自动化的KNIME工作流显著提高了COX-2抑制剂的in silico药物发现的可访问性.
  • 该工具使用户,从初学者到经验丰富的KNIME用户,能够有效地预测潜在的抑制剂.
  • 这种方法有利于在开发用于COX-2相关疾病的新疗法方面进行更广泛的研究和创新.