论文标题
通过分子模拟对生物分子和软材料的计算化合物筛选
Computational compound screening of biomolecules and soft materials by molecular simulations
论文作者
论文摘要
数十年的硬件，方法论和算法开发将分子动力学（MD）模拟推向了材料模型技术的最前沿，从而弥合了电子结构理论和连续方法之间的差距。基于物理的方法使MD适合研究新兴现象，但同时会产生大量的计算投资。该局部评论探讨了MD在单个系统范围之外的使用，而是考虑许多化合物。这样的硅筛选方法使MD可以安排建立令人垂涎的结构 - 专业关系。我们特别关注生物分子和软材料，其特征是熵贡献以及异质系统和尺度的重要作用。描述了对实施基于MD的筛选范式实施的艺术状态的说明，包括自动化力场参数化，系统制备以及跨构象和组成的有效采样。重点放在机器学习方法上，以实现基于MD的筛选。最终的框架可以生成化合物 - 范围数据库以及使用高级统计建模来收集洞察力。该评论进一步总结了许多相关应用程序。
Decades of hardware, methodological, and algorithmic development have propelled molecular dynamics (MD) simulations to the forefront of materials-modeling techniques, bridging the gap between electronic-structure theory and continuum methods. The physics-based approach makes MD appropriate to study emergent phenomena, but simultaneously incurs significant computational investment. This topical review explores the use of MD outside the scope of individual systems, but rather considering many compounds. Such an in silico screening approach makes MD amenable to establishing coveted structure--property relationships. We specifically focus on biomolecules and soft materials, characterized by the significant role of entropic contributions and heterogeneous systems and scales. An account of the state of the art for the implementation of an MD-based screening paradigm is described, including automated force-field parametrization, system preparation, and efficient sampling across both conformation and composition. Emphasis is placed on machine-learning methods to enable MD-based screening. The resulting framework enables the generation of compound--property databases and the use of advanced statistical modeling to gather insight. The review further summarizes a number of relevant applications.
