深度学习用于药物向相互作用预测:全面审查
Yingjun Chen1, Ding Luo2, Weiwei Xue2
1School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, China.
Chemical biology & drug design
|October 22, 2025
概括
深度学习 (DL) 方法通过预测药物向相互作用 (DTI) 来加速药物发现. 本综述综合了DL方法,数据集和应用程序,解决了有效DTI预测的挑战和未来方向.
科学领域:
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能
背景情况:
- 药物向相互作用 (DTI) 的预测对于药物发现至关重要,但传统上依赖于缓慢的实验方法.
- 深度学习 (DL) 为预测DTI提供了强大而高效的替代方案,加速了发现管道.
研究的目的:
- 为预测药物向相互作用提供基于深度学习的方法的结构化概述.
- 综合当前的进展,挑战和未来的研究方向在DLDTI预测DL.
主要方法:
- 对药物和蛋白质的特征表示策略的审查.
- 对各种DL架构的分析:DNN,RNN,CNN,GNN和变压器.
- 用于DTI预测的常见数据集和评估指标的摘要.
主要成果:
- 与传统方法相比,DL模型在预测DTI方面表现出显著的效率和能力.
- 探索DL在药物重新定位,药物设计和精密医学中的应用.
- 确定关键挑战,包括数据稀缺性和模型可解释性.
结论:
- 深度学习是DTI预测的变革性方法,对于现代药物发现至关重要.
- 未来的研究应该集中在自我监督学习和可解释的AI上,以克服当前的局限性.
- 本综述提供了一个全面的资源,以指导基于DL的DTI预测的未来发展.
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