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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Conserved Binding Sites01:49

Conserved Binding Sites

4.3K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.3K
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

13.4K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
13.4K

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Updated: Sep 10, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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ACLPred:一种可解释的机器学习和基于树的组合模型用于抗癌带预测

Arvind Kumar Yadav1, Jun-Mo Kim2

  • 1Functional Genomics & Bioinformatics Laboratory, Department of Animal Science and Technology, Chung-Ang University, Anseong, 17546, Gyeonggi-do, Republic of Korea.

Scientific reports
|August 25, 2025
PubMed
概括

机器学习 (ML) 通过分析分子性质来加速新抗癌药物的发现. 一种名为ACLPred的新工具使用Light Gradient Boosting Machine (LGBM) 准确预测潜在的抗癌化合物,从而节省时间和资源.

关键词:
抗癌配体癌症整体机器学习多步特征选择

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科学领域:

  • 计算化学
  • 化学信息学
  • 在药物发现中使用机器学习

背景情况:

  • 越来越多的癌症需要新的治疗药物.
  • 传统的实验性药物查需要大量资源.
  • 机器学习为识别抗癌化合物提供了快速,经济高效的替代方案.

研究的目的:

  • 开发和验证用于预测小分子抗癌活性的机器学习模型.
  • 确定对抗癌性能的关键分子特征.
  • 为研究人员创建一个可访问的工具来选潜在的候选药物.

主要方法:

  • 使用已知抗癌和非抗癌化合物的分子描述器训练分类模型.
  • 应用多步特征选择来识别重要的分子描述.
  • 采用和评估各种机器学习算法,包括光梯度增强机 (LGBM).
  • 使用SHapley添加式解释 (SHAP) 进行模型解释.

主要成果:

  • 该模型的预测准确率为90.33%,AUROC为97.31%.
  • 开发的工具ACLPred显示出比现有方法更高的预测准确性和通用性.
  • SHAP分析表明,拓分子特征显著影响了模型的预测.

结论:

  • 机器学习,特别是ACLPred中实施的LGBM算法,为识别潜在的抗癌化合物提供了有效和准确的方法.
  • ACLPred提供了一个易于使用的开源解决方案,
  • 拓特征对于预测抗癌活性至关重要,为未来的药物设计提供了洞察力.