基于结构的共价药物发现计算和人工智能驱动的生态系统.
Shi Li1, Hongyan Du1, Xujun Zhang1
1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Accounts of chemical research
|February 27, 2026
概括
这项研究介绍了用于共价药物发现的综合计算生态系统,利用人工智能 (AI) 和深度学习 (DL) 来加速新疗法的开发. 该系统连接数据库,预测模型和实验反,以实现高效的药物设计.
科学领域:
- 计算化学和化学信息学
- 药物发现和药物化学
- 人工智能在药物开发中的作用
背景情况:
- 联药物越来越重要,有超过125种药物被FDA批准.
- 深度学习 (DL) 和人工智能 (AI) 正在改变药物发现.
- 集成的计算生态系统对于实现AI在共价药物开发中的潜力至关重要.
研究的目的:
- 描述基于结构的共价药物发现的计算和人工智能驱动的生态系统.
- 突出了构建这样一个生态系统的贡献,将数据库,模型,工作流和实验反联系起来.
- 通过解决从地点识别到发现的挑战,加速下一代共价疗法的发展.
主要方法:
- 系统地收集和策划共价相关数据库.
- 开发基于人工智能/物理学的预测和评分模型,包括用于分子对接和站点预测的深度学习.
- 构建可互操作的计算工作流程,用于虚拟选和领先优化.
- 实施封闭循环反,整合实验结果以改进模型和数据库.
主要成果:
- 展示了集成生态系统加速共价药物发现的潜力.
- 展示了一个案例研究,使用虚拟选管道对共价CRM1抑制剂进行查,弥合计算预测和生物验证.
- 提供了对人工智能驱动的对接算法的性能和局限性的见解,考虑到像AlphaFold3.3这样的进步.
结论:
- 一个集成的,数据驱动的生态系统对于推动共价药物发现至关重要.
- 人工智能和计算工具,当系统地应用时,可以显著加快对共价药物候选物的识别和优化.
- 对人工智能方法的持续开发和基准测试对于未来在共价药物设计方面的突破至关重要.
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