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NfκBin:一种基于机器学习的方法,用于选TNF-α诱导的NF-κB抑制剂

Shipra Jain¹, Ritu Tomer¹, Sumeet Patiyal²

¹Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.

Frontiers in bioinformatics

|August 1, 2025

概括

我们开发了一个计算模型来预测抑制核因子kappa B (NF-κB) 信号通路的药物,这在炎症性疾病中至关重要. 该模型成功地在FDA批准的药物中确定了潜在的NF-κB抑制剂.

科学领域:

计算化学和药物发现
生物信息学和化学信息学
分子生物学和免疫学分子生物学和免疫学

背景情况:

核因子kappa B (NF-κB) 信号传递与慢性炎症性疾病 (如类风湿性关节炎,炎症性肠病和喘) 有关.
准NF-κB是开发这些疾病新疗法的关键策略.
开发NF-κB抑制剂的预测模型可以加速药物发现.

研究的目的:

开发和验证用于预测抑制TNF-α诱导NF-κB信号通路的药物的计算方法.
通过机器学习模型,从FDA批准的药物库中识别潜在的NF-κB抑制剂.
为研究NF-κB相关疾病的研究人员提供可靠的工具.

主要方法:

利用了来自PubChem的1,149种抑制剂和1,332种非抑制剂的数据集.
使用PaDEL软件和通过单变量分析和SVC-L1规范化选择相关特征的计算化学描述器.
构建和优化机器学习模型,包括支持矢量分类器,实现最大AUC为0.75.

主要成果:

使用2D和3D描述符和分子指纹的机器学习模型显示出不同的性能 (AUC0.56-0.66).
特性选择显著改善了模型性能,支持矢量分类器实现了0.75.75的AUC.
性能最好的模型成功选了FDA批准的药物,预测的抑制剂在很大程度上与已知的实验结果保持一致.

结论:

开发的计算模型证明了用于识别NF-κB抑制剂的高预测可靠性.
这种方法可以加速发现慢性炎症疾病的新疗法.
这些模型通过独立的软件和Web服务器 (NfκBin) 可用,以实现更广泛的可访问性.

关键词:

在 NF-κBB 中.化学描述符化学描述符化学描述符高通量选的高通量选抑制剂预测工具是一种抑制剂预测工具.机器学习是机器学习.核因子卡帕 B B 核因子

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