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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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...
12.8K

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相关实验视频

Updated: Jun 22, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

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

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对接分数ML:针对特定目标的机器学习模型改善了155个目标的基于对接的虚拟选.

Haihan Liu1,2,3, Baichun Hu1,2,3, Peiying Chen1,2,3

  • 1Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.

Journal of chemical information and modeling
|July 3, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了Docking Score ML,这是一种AI工具,可以提高药物发现的分子对接精度. 这种方法通过更好地模拟蛋白质-连接体相互作用来增强虚拟查和目标预测.

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Author Spotlight: Network Pharmacology and Molecular Docking to Decipher the Action of Jiawei Shengjiang San Against Diabetic Kidney Disease
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

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Author Spotlight: Network Pharmacology and Molecular Docking to Decipher the Action of Jiawei Shengjiang San Against Diabetic Kidney Disease
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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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
  • 在化学信息学中的机器学习.

背景情况:

  • 分子对接方法在精确的能量预测方面扎,原因是复杂的蛋白质 - 连接体相互作用的得分函数的限制.
  • 这种不准确性导致虚拟查和药物开发目标识别中的偏见和错误.

研究的目的:

  • 引入"对接得分ML",一个先进的得分功能,旨在克服传统分子对接的局限性.
  • 提高虚拟查和药物发现中的目标预测的准确性和效率.

主要方法:

  • 开发了"对接分数ML",使用来自155个癌症点的20多万个对接综合体.
  • 利用了来自ChEMBL的生物活性数据,并通过监督和深度学习技术进行了微调.
  • 采用功能融合,将图形卷积网络 (GCN) 集成到多个对接函数以进行增强的预测.

主要成果:

  • 使用DUDE,DUD-AD和LIT-PCBA等数据集进行了广泛的验证,证明了卓越的性能.
  • 包括sunitinib等药物在内的多目标分析证实了该方法的有效性.
  • 在精度上,GCN与对接功能的融合显著超过了传统方法.

结论:

  • "对接分数ML"比分子对接的传统方法提供了实质性的改进.
  • 该工具为虚拟查和药物发现中反向对接提供了高效和准确的解决方案.
  • 这种人工智能驱动的方法有望加速识别潜在的候选药物.