从矩阵分解到图形神经网络:在计算药物重新定位方面的进展
Yangyang Wang1, Jihan Wang2, Yanning Yang1
1School of Physics and Electronic Information, Yan'an University, Yan'an 716000 Shaanxi, China.
Drug discovery today
|October 11, 2025
概括
人工智能 (AI) 通过使用先进模型分析生物医学数据来加速药物重新定位. 这种方法有助于发现各种疾病的新疗法,同时解决数据集成和安全方面的关键挑战.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 医学中的人工智能
背景情况:
- 药物重新定位为传统药物发现提供了一种具有成本效益的替代方案.
- 人工智能 (AI) 和计算生物学正在通过大规模的生物医学数据分析加速药物重新定位.
研究的目的:
- 审查人工智能驱动的药物重新定位战略.
- 突出AI在各种疾病领域的应用.
- 讨论计算药物发现的挑战和未来方向.
主要方法:
- 审查人工智能策略,包括矩阵分解,图形神经网络和变压器.
- 分析in silico预测和现实世界的验证.
- 检查数据异质性和模型可解释性等挑战.
主要成果:
- 人工智能模型在瘤学,传染病,神经学,自身免疫和罕见疾病方面展示了成功的应用.
- 吸收,分布,新陈代谢,分泌和毒性 (ADME-Tox) 的整合以及安全性对于减少晚期磨损至关重要.
- 多omics集成,基础模型和生成AI代表了未来的方向.
结论:
- 人工智能通过计算方法显著增强了翻译性药物发现.
- 人工智能在治疗创新中发挥着关键作用,因为它可以有效地重新定位药物.
- 解决数据和安全挑战是最大限度地发挥AI在药物开发中的潜力的关键.
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