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多药理学预测:通往全面预测小分子选择性的漫长道路,以降低药物发现风险
Leticia Manen-Freixa1, Albert A Antolin1,2
1Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain.
预测多药理学,即小分子结合多个标,对于药物发现至关重要. 计算方法提供了一种具有成本效益的方法来预测这些相互作用,提高药物安全性和有效性.
科学领域:
- 药理学 药理学是指药理学的学科.
- 化学信息学 化学信息学
- 生物信息学是一种生物信息学.
背景情况:
- 小分子可以结合多个标,这种现象被称为多药理学,影响药物的安全性和有效性.
- 评估药物选择性的实验方法存在局限性,导致由于未预见的非目标效应而导致临床失败.
- 计算方法提供了一个互补的和具有成本效益的方法来预测多药理学和识别潜在的目标.
研究的目的:
- 提供对多药学预测方法的全面概述.
- 讨论当前计算方法的优点和局限性.
- 审查用于多药学预测的数据源和算法.
主要方法:
- 复习经典化学信息学和生物信息学方法.
- 分析可用的数据源及其覆盖范围.
- 讨论基于数据类型的主要算法,并举例.
主要成果:
- 多药理学预测已经进步,有助于目标以外的识别.
- 数据不完整性目前限制了全面的选择性预测.
- 模型评估协议是有限的,影响识别具有适度现实世界性能的最佳算法.
结论:
- 尽管存在局限性,但大数据和人工智能 (AI) 的进步显示出改善多药学预测的前景.
- 增强的预测可以通过早期识别潜在的安全性和有效性问题,帮助降低药物发现的风险.
- 持续开发计算方法对于成功的药物发现至关重要.
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