药物研发中的人工智能:现状、进展与未来展望
AI in drug discovery — what it is, where we stand and the path forward

原始链接: https://www.nature.com/articles/s41573-026-01496-2

此文献集梳理了人工智能(AI)与机器学习(ML)在制药行业中的演变与融合。所列参考文献涵盖了从深度学习(如 CNN 和 LSTM)的基础性发展,到其在药物研发、蛋白质结构预测(如 AlphaFold)及临床试验优化等高难度领域的前沿应用。 核心主题包括: * **方法论的进步:** 从传统计算化学向深度生成模型、强化学习及大语言模型的转变,旨在实现从头药物设计(de novo molecular design)与生物系统建模。 * **药物发现与开发:** AI 在靶点识别、表型筛选(如 Cell Painting)以及“设计-合成-测试-分析”(DMTA)循环优化中的应用。 * **实施中的挑战:** 对当前 AI 实践的关键评估,强调了“可重复性危机”、建立稳健基准测试(如 CASP、CACHE)的必要性,以及数据泄露和模型过拟合带来的风险。 * **未来展望:** 通过整合多组学、电子健康记录及“器官芯片”模型,旨在弥合计算机模拟预测与临床成功之间的鸿沟,从而改善这一历来高失败率领域的研发生产力与决策水平。

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