使用分离拓方法对基于碎片的化合物进行高效的绑定亲和度估计
Ana-Maria Caldaruse1, Hannah M Baumann2, David L Mobley1
1School of Pharmacy and Pharmaceutical Sciences, University of California, Irvine, California 92697, United States.
Journal of chemical information and modeling
|March 13, 2026
概括
基于碎片的药物发现 (FBDD) 从准确的结合亲和力预测中受益. 分离拓 (SepTop) 方法在建模片段转换和优化药物线索方面显示出前景.
科学领域:
- 计算化学计算化学
- 药物发现 药物发现 药物发现
- 生物物理学的生物物理.
背景情况:
- 基于碎片的药物发现 (FBDD) 对早期药物开发至关重要.
- 预测碎片和类型的结合亲和关系,由于弱相互作用和结构多样性,会带来计算挑战.
- 现有的自由能源方法往往没有针对FBDD的特定需求进行优化.
研究的目的:
- 评估分离拓 (SepTop) 方法用于模拟药物发现中的基于片段的转换.
- 评估SepTop在预测碎片合并和链接的绑定亲缘关系方面的准确性.
- 确定SepTop适合在早期药物开发中进行片段优化.
主要方法:
- 使用已建立的FBDD数据集进行回顾性分析 (Cyclophilin D,SARS-CoV-2 Macrodomain 1).
- 分离拓的应用 (SepTop) 计算方法.
- 对碎片和类化合物进行具有约束力的自由能量计算的验证.
主要成果:
- 该SepTop方法准确地恢复了实验性的结合亲和关系.
- 在碎片和类化合物中观察到良好的预测准确性.
- SepTop在模拟碎片合并和链接转换方面表现出有效性.
结论:
- SepTop是FBDD中片段优化的合适方法.
- SepTop可以将绑定自由能量计算的应用扩展到更早的药物发现阶段.
- 这种方法增强了开发新疗法的计算策略.
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