关于用于药物标结合预测的深度学习的调查:模型,基准,评估和案例研究
Kusal Debnath1, Pratip Rana2, Preetam Ghosh1
1Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States.
Briefings in bioinformatics
|September 22, 2025
概括
人工智能 (AI) 通过改善药物标结合 (DTB) 预测,显著加速药物发现. 这篇评论详细介绍了人工智能.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 人工智能的人工智能
背景情况:
- 传统的药物发现是昂贵的,缓慢的,并且经常失败.
- 人工智能 (AI) 为复杂的生物问题提供先进的解决方案.
- 药物标结合 (DTB) 对于有效的药物发现至关重要.
研究的目的:
- 审查最近用于药物标结合 (DTB) 预测的深度学习模型.
- 分析人工智能方法在DTB预测中的演变.
- 确定人工智能驱动药物发现的挑战和未来方向.
主要方法:
- 分析深度学习模型,基准数据集和用于DTB预测的指标.
- 检查人工智能方法:异质网络,基于图形,基于注意力和多式联络方法.
- 使用化合物库对抗与癌症相关的蛋白质标的案例研究.
主要成果:
- 深度学习已经彻底改变了药物发现,从基于网络的方法转向了先进的AI架构.
- 证明了人工智能在预测药物向相互作用和亲缘关系方面的实用性.
- 确定了当前DTB预测模型的局限性.
结论:
- 深度学习为理解药物目标关系提供了定量框架.
- 人工智能显著加快了新药候选药物的识别.
- 未来的研究应该专注于改进人工智能模型,以实现更准确的DTB预测.
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