基于NMR的快速和可靠的药物发现碎片评分
Ridvan Nepravishta1, Juan C Munoz-Garcia2, Kenneth Cameron1
1Cancer Research Horizons, CRUK Scotland Institute, Garscube Estate, Switchback Road, Bearsden, Glasgow G61 1BD, U.K.
Journal of the American Chemical Society
|October 23, 2025
概括
我们开发了ML-boosted SHARPER NMR来加快基于碎片的药物发现. 这种方法可以快速量化碎片结合 afinities,加速潜在的药物线索的识别.
科学领域:
- 生物化学和结构生物学
- 医学化学
- 生物物理
背景情况:
- 基于碎片的药物发现 (FBDD) 通过选分子碎片与蛋白质标来识别新疗法.
- 核磁共振 (NMR) 光谱对于检测FBDD中的弱蛋白-配体相互作用至关重要.
- 传统的核磁共振方法测量结合性 (KD) 是耗时且劳动密集的.
研究的目的:
- 在FBDD中开发创新和高效的碎片评分方法.
- 使用NMR加快碎片结合亲和度 (KD) 的量化.
- 整合机器学习与NMR以实现更快的合并转换.
主要方法:
- 开发了一种SHARPER核磁共振技术,用于灵敏地检测弱蛋白-连接体相互作用.
- 实施了机器学习模型,使用最小的SHARPER定位数据点对片段的亲和度进行排序.
- 将机器学习集成到"ML-boosted H LB SHARPER NMR"方法中.
主要成果:
- SHARPER NMR技术显著减少了KD测量的数据采集时间.
- 用ML增强的方法只使用两个定位点准确地排列碎片亲和度.
- 在一天内确定了多达144个KD值,这比传统方法有了显著的改善.
结论:
- 强化ML的H LB SHARPER核磁共振方法在FBDD中可节省大量时间.
- 这种方法加快了碎片结合的评估,使早期药物发现的决策更快.
- 该方法简化了从初始碎片到化合物的过渡.
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