将潜在函数调整为主机-客户端绑定数据
Jeffry Setiadi1, Simon Boothroyd2,3, David R Slochower4
1Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9255 Pharmacy Lane, La Jolla, California 92093, United States.
Journal of chemical theory and computation
|December 26, 2023
概括
这项研究引入了一种使用宿主-客人系统来训练力场的新方法,以更准确地预测蛋白质-连接体结合亲缘关系. 优化的参数显示了更好的结合预测和更快的计算,有助于药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 分子建模分子建模
背景情况:
- 在药物发现中,基于物理学的方法对于预测蛋白质-连接体结合亲缘关系至关重要.
- 这些方法的准确性在很大程度上取决于用于模拟的潜在函数质量.
- 实验性的结合亲缘关系尚未被广泛用于训练这些潜在的功能.
研究的目的:
- 扩展开放力场评估器框架,用于计算主机-客户结合的自由能量.
- 策划实验性主机-客户绑定数据,用于训练潜在的功能.
- 使用主机-客户数据,为OBC2隐性溶剂模型优化概括的Born (GB) 腔半径.
主要方法:
- 系统计算主机-客户结合的自由能量及其梯度.
- 策划126个宿主-客人综合体的实验性结合自由能量.
- 针对36个主机-客户系统,优化OBC2 GB模型空腔半径.
主要成果:
- 主机-客户系统的训练和测试集的精度大幅提高.
- 优化半径从宿主-客人到蛋白质-连接体系统的证明可转移性.
- 确定了精确的结合亲和度和具有优化半径的无水化能量之间的权衡.
结论:
- 主机-客机系统可以有效地训练可转移的潜在函数,用于蛋白质-连接体结合亲缘关系的预测.
- 开发的基础设施使得计算药物发现的新应用成为可能.
- 隐式溶剂模型为具有约束力的自由能量计算提供了显著的加速,这表明虚拟选的实用性.
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