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BBB-PEP预测:使用混合位置相对组成的特定特征和集合建模来识别血脑屏障的改进计算模型

Ansar Naseem¹, Fahad Alturise², Tamim Alkhalifah³

¹Department of Artificial Intelligence, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan.

Journal of cheminformatics

|November 19, 2023

概括

这项研究介绍了BBB-PEP-Prediction,这是用于识别血脑屏障 (BBB) 的先进计算模型. 该模型通过准确预测-BBB相互作用来增强中枢神经系统 (CNS) 疾病的药物输送.

科学领域:

生物化学生物化学
计算生物学计算生物学
药理学药理学是指药理学的学科.

背景情况:

血脑屏障 (BBB) 显著阻碍药物输送到中枢神经系统 (CNS),影响神经系统疾病的治疗.
超过98%的小分子和近100%的大分子药物无法有效地穿透BBB.

研究的目的:

开发一个改进的智能计算模型,BBB-PEP-Prediction,用于准确识别血脑屏障 (BBB) .
提高能够穿越BBB的的预测,以改善中枢神经系统的药物输送.

主要方法:

从基准数据集中利用基于位置和统计时刻的特征.
采用组合方法,包括包装 (随机森林,额外树木),提升 (XGBoost,LightGBM) 和堆叠/混合与后勤回归作为一个meta-learner.
使用随机搜索CV优化了LightGBM,XGBoost和额外树木分类器.
通过自我一致性,独立集,交叉验证 (5和10折叠) 和刀测试来评估模型性能.

主要成果:

堆叠组合方法在各种测试策略中表现出卓越的性能.
在独立测试时,堆叠模型的精度为0.824,特异性为0.911,灵敏度为0.831,MCC为0.663.
在识别BBB上,BBB-PEP-Prediction的表现优于之前的基准研究.

结论:

开发的BBB-PEP-Prediction模型显示了BBB的体内识别的巨大潜力.
这种计算方法可以帮助研究人员开发用于中枢神经系统疾病的新型治疗策略,通过促进跨BBB的药物输送.

关键词:

数据挖掘数据挖掘组合建模组合建模机器学习机器学习模式识别模式识别类别分类类别分类序列分析序列分析监督学习监督学习转移学习人工智能人工智能

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