人工智能简化了对药物向相互作用的科学发现
Yuxin Yang1,2, Feixiong Cheng1,2,3,4
1Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
British journal of pharmacology
|January 22, 2025
概括
人工智能 (AI) 通过预测药物向相互作用 (DTI) 来加速药物发现. 本综述探讨了人工智能方法,数据库和验证技术,以提高治疗开发效率.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物发现是昂贵和低效的,药物向相互作用 (DTI) 的识别是关键的瓶.
- 传统的DTI识别实验方法的成功率很低,成本很高.
- 计算方法,特别是人工智能 (AI),为简化DTI预测提供了一个有希望的方法.
研究的目的:
- 审查人工智能技术在预测药物向相互作用 (DTI) 的应用.
- 提供药物发现,DTI预测和验证的全面概述.
- 探索用于DTI预测的最先进的AI方法.
主要方法:
- 在DTI预测中对AI应用进行系统审查.
- 分析着名的数据库和工具包用于AI驱动的DTI预测.
- 探索经典机器学习,深度学习和基于网络的AI方法.
主要成果:
- 人工智能方法显著提高了DTI预测的效率并降低了DTI预测的成本.
- 经典机器学习,深度学习和基于网络的方法在DTI预测中表现出明显的优势.
- 数据库和验证方法对于开发和评估AI模型至关重要.
结论:
- 人工智能是药物发现的变革性工具,提高了DTI预测的准确性和速度.
- 需要继续进行研究,以应对当前的挑战,并释放AI在药物开发中的未来潜力.
- 人工智能驱动的DTI预测对加速开发新疗法具有重大前景.
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