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精选和基于结构的药物向相互作用改善了网络模型中药物副作用的低预测
Mohammadali Alidoost1, Amy Le1, Jennifer L Wilson1
1Department of Bioengineering, University of California, Los Angeles (UCLA), 410 Westwood Plaza, Los Angeles, California 90095, United States.
预测药物的副作用对于制药开发至关重要. 整合多种药物向数据源可以提高预测准确性,平衡敏感性和特异性,以便更好地评估药物安全性.
科学领域:
- 药理学和药物开发领域
- 计算生物学 计算生物学
- 毒理学 毒理学 毒理学
背景情况:
- 准确预测药物诱导的副作用是制药开发中的一个主要障碍,往往导致后期失败.
- 传统的方法,如动物试验和体外测试,在成本,伦理和人类可翻译性方面都有局限性.
- 现有的用于预测不良药物效应的计算模型,例如蛋白质-蛋白质相互作用网络,往往受到预测不足和数据不一致的影响.
研究的目的:
- 评估将来自多个来源的药物结合标集成到PathFX平台的影响,以改善药物副作用预测.
- 分析不同数据整合策略如何影响预测药物不良反应的敏感性和特异性.
- 为提高药物安全性评估中的机器学习方法提供基础.
主要方法:
- 将六个不同的数据库 (DrugBank,ChEMBL,PubChem,STITCH,TTD,PocketFEATURE) 的药物结合目标集成到PathFX平台中.
- 分析了跨集成数据源的独特药物向相互作用,蛋白质类和功能.
- 基于数据源特征,量化评估了基于数据源特征的预测敏感性和特异性之间的权衡.
主要成果:
- 整合新的药物标导致了预测以前未被识别的副作用.
- 观察到灵敏度和特异性之间的明显权衡;更大,探索性数据库以特异性为代价提高了灵敏度.
- 较小,精选或结构预测的目标数据库增强了具体性,适合PathFX等可解释平台.
结论:
- 整合多种药物向数据源显著影响药物副作用预测的准确性.
- 在预测模型中,选择数据源和整合策略对于平衡敏感性和特异性至关重要.
- 这项工作支持开发复杂的机器学习模型,用于大规模数据和更简单,可解释的平台,用于制造药物安全假设.
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