通过多地图有针对性的估计,从力场获得QM准确度的自由能量
Andrea Rizzi1,2, Paolo Carloni1,3, Michele Parrinello2
1Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany.
概括
这项研究引入了一种有效的计算方法,用于使用量子力学 (QM) 和力场 (FFs) 预测连接体结合亲缘关系. 这种新的方法通过降低QM免费能源计算的成本,显著加速药物发现.
科学领域:
- 计算化学计算化学
- 分子建模分子建模
- 药物发现 药物发现 药物发现
背景情况:
- 准确预测连接体结合亲缘关系对于加速药物发现至关重要.
- 目前使用量子力学 (QM) 自由能量计算的方法在计算上昂贵,并且在早期药物发现方面基本上是不可行的.
- 从力场 (FFs) 调整到QM潜力的缓慢趋同是一个重要的瓶.
研究的目的:
- 开发一种高效的方法来计算QM自由能量,使用廉价的参考潜力,如FFs.
- 为了克服QM/FF免费能源计算中的缓慢趋同问题.
- 为了加速对连接体结合亲缘关系的预测,用于药物发现应用.
主要方法:
- 使用与规范化流神经网络 (NN) 实现的多个地图的目标自由能源方法的概括.
- 通过一个时代的学习政策来有效培训NN,以避免过度匹配和最大限度地实现分布重叠.
- 与增强的抽样策略的整合,以应对缓慢的自由度和改善趋同.
主要成果:
- 与标准的自由能量扰动相比,该方法将QM自由能量计算加快三次数.
- 与之前在HiPen数据集上的不平衡计算相比,实现了八倍的加速.
- 从使用FF参考电位的模拟中证明了QM自由能量的高效计算.
结论:
- 开发的方法显著降低了QM自由能源计算的计算成本.
- 这种方法使QM准确性在优化活动和蛋白质-连接体结合研究中能够得到使用.
- 该方法为将高精度的QM潜力整合到早期药物发现中提供了一条可行的途径.
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