关于药物向相互作用预测的综合性审查 - 最新进展和概述
Ali K Abdul Raheem1,2, Ban N Dhannoon3
1Software Department, College of Information Technology, University of Babylon, Hillah, Babil, Iraq.
Current drug discovery technologies
|September 8, 2023
概括
预测药物向相互作用 (DTI) 通过识别分子关联来加速药物发现. 机器学习方法提供了高效的计算方法来克服DTI预测中的实验限制.
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
- 药物发现 药物发现 药物发现
背景情况:
- 药物向相互作用 (DTI) 对于调节生物功能至关重要,是药物开发的基础.
- 识别DTI的实验方法耗时且昂贵,需要计算方法.
- 准确的DTI预测可以显著提高效率并降低与药物发现相关的成本.
研究的目的:
- 提供药物向相互作用的全面概述,作为药物发现的关键初步步骤.
- 探索机器学习方法在预测DTI中的应用和有效性.
- 审查用于DTI预测领域的相关文献和数据库.
主要方法:
- 审查现有的文献和数据库,重点关注药物向相互作用.
- 探索用于DTI预测的计算方法,包括对接模拟,基于联体的方法和机器学习技术.
- 机器学习应用程序的分析,用于预测药物与其生物点之间的关联.
主要成果:
- 药物向相互作用的预测是药物发现的重要领域,影响效率和降低成本.
- 与传统的实验分析相比,机器学习方法为加速DTI预测提供了一个有希望的途径.
- 该综述强调了DTI预测的多样化应用,包括药物发现,不良影响预测和药物重新定位.
结论:
- 基于机器学习的DTI预测是简化药物开发早期阶段的关键策略.
- 计算方法,特别是机器学习,对于克服实验性DTI识别的局限性至关重要.
- 进一步研究和应用这些方法可以显著缩短新疗法上市的时间.
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