基于体外活动概况的药物标的预测,用于用于罕见疾病的药物重新定位
Binghan Xue1, Ruili Huang2, Qian Zhu2
1Division of Rare Diseases, Research Innovation, National Institutes of Health, Bethesda, U.S.
概括
药物重新定位可以加速罕见疾病的治疗. 这项研究开发了机器学习模型来预测化合物的基因标,有助于发现罕见疾病的新疗法.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 毒理学 毒理学 毒理学
背景情况:
- 全球有超过3亿人患有罕见疾病,往往治疗选择有限.
- 药物再利用提供了一种可行的策略,通过确定现有药物的新用途来发现新的治疗方法.
- 预测化学化合物的基因标对于推进药物重定向努力至关重要.
研究的目的:
- 开发和评估用于预测化学化合物基因标的机器学习模型.
- 扩展之前在21世纪毒理学 (Tox21) 图书馆中对化合物的丰富基因的识别工作.
- 通过发现新型的基因化合物关系,促进用于罕见疾病的药物重新用途.
主要方法:
- 开发了机器学习模型,包括支持向量机,K-最近邻居,随机森林和极端梯度增强 (XGBoost).
- Tox21生物试验查数据被用于训练和评估预测模型.
- 在XGBoost模型中测试了四种多标签预测嵌入算法:二进制相关性,标签权力集,分类器链和多输出分类器.
主要成果:
- 所有四个开发的机器学习模型都表现出强的性能,f1得分超过0.7.
- 极端梯度增强 (XGBoost) 模型在评估的模型中取得了最佳性能.
- 这项研究成功地探索了一种可靠的方法,可以从体外活性资料中预测潜在的基因标.
结论:
- 机器学习模型,特别是XGBoost,可以从体外活动数据中可靠地预测基因标.
- 这种方法通过确定现有化合物的潜在新用途来支持药物的重新用途.
- 这些发现有助于发现用于罕见疾病的新型治疗方法.
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