Toward Learned Chemical Perception of Force Field Typing Rules
Toward Learned Chemical Perception of Force Field Typing Rules
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DOI：
10.1021/acs.jctc.8b00821
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发表时间：
2019-01-01
影响因子：
5.5
通讯作者：
Mobley, David L.
中科院分区：
文献类型：
--
作者：
Zanette, Camila;Bannan, Caitlin C.;Mobley, David L.
分子力学力场定义了如何从原子位置计算分子系统中的能量和力，从而通过分子动力学和蒙特卡罗模拟等计算方法来研究此类系统。尽管在自动力场参数化方面取得了进展，但需要相当多的人类专业知识来开发或扩展力场。特别是，长期以来，人类输入一直需要定义原子类型，这些类型编码化学上独特的环境，这些环境决定了哪些参数将被分配。然而，依靠人类来建立原子类型是次优的。人类创造的原子类型通常是在没有统计依据的情况下开发的，导致数据的过度拟合或拟合不足。当新的化学物质必须被建模或新的数据变得可用时，人类创造的类型也难以以系统和一致的方式扩展。最后，当必须为新的（生物）聚合物、化合物类或材料生成力场时，人类的努力是不可扩展的。为了弥补这些缺陷，我们的长期目标是用自动化方法取代人类对原子类型的规范，该方法基于严格的统计数据，并由实验和/或量子化学参考数据驱动。在这项工作中，我们描述了自动发现适当的化学感知的新方法：SMARTY允许创建原子类型，而SMIRKY则通过自动创建片段（非键合，键，角度和扭转）类型而更进一步。这些方法使得能够在原子或碎片类型空间中创建移动集合，其在蒙特卡罗优化方法中使用。我们通过自动化重新发现现有小分子力场中人类定义的原子类型（SMARTY）或片段类型（SMIRKY）来展示这些新方法的强大功能。我们评估这些方法使用几个分子数据集，包括一个涵盖了不同的子集的DrugBank数据库。
Molecular mechanics force fields define how the energy and forces in a molecular system are computed from its atomic positions, thus enabling the study of such systems through computational methods like molecular dynamics and Monte Carlo simulations. Despite progress toward automated force field parametrization, considerable human expertise is required to develop or extend force fields. In particular, human input has long been required to define atom types, which encode chemically unique environments that determine which parameters will be assigned. However, relying on humans to establish atom types is suboptimal. Human-created atom types are often developed without statistical justification, leading to over- or under-fitting of data. Human-created types are also difficult to extend in a systematic and consistent manner when new chemistries must be modeled or new data becomes available. Finally, human effort is not scalable when force fields must be generated for new (bio)polymers, compound classes, or materials. To remedy these deficiencies, our long-term goal is to replace human specification of atom types with an automated approach, based on rigorous statistics and driven by experimental and/or quantum chemical reference data. In this work, we describe novel methods that automate the discovery of appropriate chemical perception: SMARTY allows for the creation of atom types, while SMIRKY goes further by automating the creation of fragment (nonbonded, bonds, angles, and torsions) types. These approaches enable the creation of move sets in atom or fragment type space, which are used within a Monte Carlo optimization approach. We demonstrate the power of these new methods by automating the rediscovery of human defined atom types (SMARTY) or fragment types (SMIRKY) in existing small molecule force fields. We assess these approaches using several molecular data sets, including one which covers a diverse subset of the DrugBank database.