相关实验视频

Updated: Apr 12, 2026

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

Published on: June 4, 2021

用机器学习辅助的基于碎片的计算设计来发现新的高血压小分子的数据集

Odifentse Mapula-E Lehasa¹, Uche A K Chude-Okonkwo¹

¹Institute for Intelligent Systems, University of Johannesburg, 69 Kingsway Avenue, Auckland Park, Johannesburg 2092, Gauteng Province, South Africa.

|July 29, 2024

概括

这项研究使用了计算方法和机器学习来创建高血压的新药,特别针对宁-血管酶-阿尔多斯特系统 (RAAS). 产生的分子包括新型的 ангиотензин转化酶抑制剂 (ACEI) 和 ангиотензинII受体阻断剂 (ARB).

科学领域:

计算化学和化学信息学
药理学和药物发现
机器学习在药物化学中的应用.

背景情况:

高血压的管理通常涉及向宁-血管酶-阿尔多斯特系统 (RAAS).
已批准的药物包括 ангиотензин转化酶抑制剂 (ACEI) 和 ангиотензинII受体阻断剂 (ARB).
对高血压的新药发现需要有效的方法来产生各种分子.

研究的目的:

证明基于计算碎片化和机器学习辅助的药物发现对产生新型抗高血压剂的实用性.
创建针对RAAS的新分子,特别是ACEI和ARB.
提供数据集,可以加快对高血压新药的设计.

主要方法:

一个初步的数据集由63个分子碎片从已批准的ACEI和ARB分子编制.从ChEMBL和DrugBank编制.
使用计算碎片化和机器学习方法生成了新的分子.
生成的分子被选为口服药物标准和ACEI/ARB功能组的存在,然后使用无监督机器学习进行集群.

主要成果:

新生成分子的三个不同的数据集被产生:新型ACEI,新型ARB和未分配类分子.
该过程成功地确定了具有理想药理性质的潜在新化合物.
机器学习集群有效地根据功能组分配对分子进行分类,与药物类别保持一致.

结论:

计算碎片化和机器学习为发现新型抗高血压药物线索提供了有效的策略.
生成的数据集可以显著帮助及时设计新的抗高血压药物.
开发的模型可以适应产生分子,用于高血压以外的其他治疗领域.

关键词:

抗胰岛素II受体阻断剂抗胰岛素II受体阻断剂抗氨酸转化酶抑制剂抗氨酸转化酶抑制剂基于碎片的药物发现.高血压是因为高血压.K-Means集群是指一个集群.雷宁- ангиотензин-阿尔多斯特系统

更多相关视频

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

相关概念视频

Drug Discovery: Overview

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...

Structure-Activity Relationships and Drug Design

Structure-Activity Relationships and Drug Design

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...

Pharmacogenomics: Identification of New Drug Targets

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...