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MARVpred:针对马尔堡病毒的抑制剂的机器学习预测基因 4 小ORF蛋白质

Eugene Lamptey¹, Gabriel Anyaele², Harry Arthur²

¹West African Center for Cell Biology of Infectious Pathogens, College of Basic and Applied Sciences, University of Ghana, Accra, Ghana. lampteyeugene8@gmail.com.

BMC infectious diseases

|February 5, 2026

概括

机器学习模型通过准4基因小ORF蛋白来识别潜在的马尔堡病毒抑制剂. 网络应用MARVpred加速了对这种致命的出血性发烧病毒的治疗方法的发现.

科学领域:

计算生物学和病毒学.
药物发现和药物化学
机器学习在卫生中的应用.

背景情况:

马尔堡病毒 (MARV) 引起严重的出血热,死亡率高,对公共健康构成重大风险.
马尔夫基因4小ORF蛋白对病毒复制和免疫逃避至关重要,使其成为关键的治疗点.

研究的目的:

使用机器学习识别马尔堡病毒基因4小ORF蛋白的潜在抑制剂.
开发一种用于选抗MARV化合物的预测模型,加快治疗开发.

主要方法:

利用PubChem的301,745种化合物的大型数据集进行模型训练.
采用了各种机器学习算法,包括随机森林 (RF),梯度增强机器 (GBM),CatBoost,AdaBoost和物流回归.
使用 RDKit 和 PaDEL 生成的分子描述器,摩根指纹表现出比 PubChem 指纹更高的性能.

主要成果:

随机森林和梯度增强机表现最好,RF实现了83%的特异性和0.84的ROC-AUC.
与PubChem指纹相比,摩根指纹的准确度 (76%),精度 (80%) 和ROC-AUC (84%) 都更高.
经过验证的模型显示了强大的预测可靠性,用于识别潜在的马尔堡病毒抑制剂.

结论:

机器学习可以有效地预测具有抗MARV特性的化合物.
开发的MARVpred网络应用程序为加速发现马尔堡病毒治疗方法提供了宝贵的工具.
这种计算方法显著推动了对马尔堡出血热的有效治疗方法的研究.

关键词:

人工智能的人工智能是人工智能.药物发现药物发现基因4 小型ORF蛋白质机器学习机器学习马尔堡病毒 (MARV) 是一种马尔堡病毒疾病 (MVD)

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