高通量合物解离动力学预测使用合物竞争和的位点识别
Wenbo Yu1,2,3, Shashi Kumar1, Mingtian Zhao1
1Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States.
Journal of chemical theory and computation
|April 26, 2025
概括
预测药物脱离率 (k_off) 对于药物设计至关重要. 一种新的联合物理和机器学习 (ML) 方法有效地估计了连接体解离动力学,克服了传统模拟的局限性.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 在药理学中的机器学习.
背景情况:
- 药物解离率 (k_off) 对疗效至关重要,通常比结合亲和力更重要.
- 目前的预测方法依赖于计算上昂贵的分子动力学 (MD) 模拟,阻碍了大规模的药物设计.
- 需要有效的计算工具来预测药物标结合动力学.
研究的目的:
- 开发和验证一种联合物理和机器学习 (ML) 方法,用于预测药物分子解离率 (k_off).
- 在药物设计中创建一个计算高效的工作流来研究连接体解离动力学.
- 为了使原子和功能组对连接物解离的贡献能够进行定量估计.
主要方法:
- 通过连接物竞争和 (SILCS) 方法识别位点,以确定连接物解离路径并计算自由能量概况.
- 采用机器学习模型 (基于树的和神经网络) 在SILCS衍生的自由能量配置文件和分子性质上进行训练.
- 通过在13种不同的蛋白质中使用329个配体验证了工作流.
主要成果:
- 开发的工作流,SILCS-Kinetics,在预测k_off值方面表现出强度和效率.
- 基于基于物理的自由能量配置文件,ML模型准确地预测了解离动力学.
- 该方法成功量化了特定分子特征对联体解离的贡献.
结论:
- SILCS-Kinetics工作流提供了一个强大而高效的工具,用于研究连接体解离动力学.
- 与传统的MD模拟相比,这种方法在药物设计中显著加快了对联体的评估.
- 工作流程为药物解离动力学进行定量结构-活性关系 (QSAR) 研究提供了便利.
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