医学生物学和人工智能药物发现之间的差距
Yueming Yin1, Afu Fu1, Lipo Wang2
1Institute for Digital Molecular Analytics and Science, Nanyang Technological University, 636921, Singapore.
Drug discovery today
|November 7, 2025
概括
人工智能 (AI) 可以加速药物发现,但往往无法与生物现实保持一致. 这项研究确定了人工智能驱动药物发现的关键差距,并提出了一个生物上语境化的AI框架,以改善其与医学生物学研究的整合.
科学领域:
- 药物发现 药物发现
- 计算生物学是一种计算生物学.
- 医学中的人工智能
背景情况:
- 药物发现是一个复杂的,多学科的过程.
- 人工智能 (AI) 具有加速药物发现的潜力.
- 目前在药物发现中的人工智能应用往往与生物现实不一致.
研究的目的:
- 确定当前人工智能驱动的药物发现中的关键差距.
- 提出一个生物上下文化的人工智能框架.
- 为整合人工智能与医学生物学提供准则.
主要方法:
- 在人工智能驱动的药物发现中分析常见的陷.
- 确定三个关键的差距:结合亲和力与生物活性,简化生物活性指标和碎片化的AI应用.
- 开发一种新的AI框架,强调生物背景.
主要成果:
- 将结合性与生物活性的结合性混为一谈,忽略了不同的实验背景.
- 简化的生物活性指标限制了AI捕获复杂生物信息的能力.
- 碎片化的AI工具阻碍了跨药物发现管道的整体整合.
结论:
- 解决这些差距对于有效地将AI整合到药物发现中至关重要.
- 一个生物上语境化的AI框架可以弥合AI预测和生物验证之间的差距.
- 提供了指导方针,以促进人工智能研究人员和医学生物学家之间的合作.
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