关于药物相互作用预测的深度和图形学习的全面评估
Xuan Lin1, Lichang Dai1, Yafang Zhou1
1College of Computer Science, Xiangtan University, Xiangtan, China.
Briefings in bioinformatics
|July 4, 2023
概括
人工智能 (AI) 和深度学习模型加速药物相互作用 (DDI) 的预测,克服了传统的实验限制. 本综述指导研究人员使用人工智能方法,在药物发现中进行高效的DDI分析.
科学领域:
- 生物医学应用程序
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物相互作用 (DDI) 在药物发现和临床实践中至关重要.
- 通过传统方法预测DDI是昂贵且耗时的.
- 人工智能和深度学习为DDI预测提供了有希望的替代方案.
研究的目的:
- 审查和指导研究人员在DDI预测中应用AI,深度学习和图形学习.
- 要总结用于DDI预测的各种计算方法.
- 讨论人工智能驱动的DDI预测中的挑战和未来方向.
主要方法:
- 对基于化学结构,基于网络,基于自然语言处理和混合方法进行DDI预测的审查.
- 介绍分子表示技术.
- 解释图形神经网络 (GNN) 框架用于分子结构分析.
- 评估深度和图形学习方法的比较实验.
主要成果:
- 人工智能和图形学习模型为DDI预测提供了有效的方法.
- 比较实验突出了不同深度和图形学习技术的优缺点.
- 确定的挑战包括数据可用性,编码和计算方法设计.
结论:
- 深度和图形学习模型可以加速DDI预测.
- 应对技术挑战和探索未来的方向对于推动AI在这个领域的发展至关重要.
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