Accurate Molecular Mechanics Force Fields through Data-driven Parameter Type Definitions

通过数据驱动的参数类型定义精确的分子力学力场

• 批准号: 462118626
• 负责人: Dr. Tobias Hüfner
• 金额: --
• 依托单位: Abteilung Theoretische Biophysik
• 依托单位国家: 德国
• 项目类别: WBP Position
• 财政年份: 2021
• 资助国家: 德国
• 起止时间: 2020-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/462118626?language=en
• 关键词: Accurate; Molecular; Mechanics; Force; Fields

Molecular processes are complex and only a fraction of their details is discernable by experimental techniques. However, there are many applications in which it is of high interest to be able to predict the relevant molecular details. In this context, atomistic simulations have become increasingly important to probe the properties and interactions of (bio)molecules. Although these simulations can be theoretically sound, they are not necessarily accurate, and a key source of error is the underlying molecular mechanics force field, which relates a given molecular structure to atomic forces. Today, a major hurdle to improving force fields is the lack of rigor in the schemes used to cast atoms into categories for assignment of force field parameters. These categories, which are commonly termed parameter types, group similar chemical environments (i.e. substructures) and assign a common set of parameters to the atoms within these chemical environments. To avoid overfitting and facilitate parameter optimization, these types should be as few as possible while still enabling good agreement between computed and reference (experimental or high-level quantum chemistry calculation) molecular properties. However, parameter types have historically been assigned in a largely ad hoc manner. This prevents the rigorous optimization of force field parameters as new reference data becomes available and the straightforward introduction of new chemical substructures into existing force fields. Here, I propose a novel approach that overcomes the aforementioned obstacles through the combined data-driven optimization of force field parameter type definitions and force field parameter values. The approach is fundamentally different from existing force field optimization approaches that only tuned or added parameters to a given force field. In the proposed project, Bayesian inference and Monte Carlo sampling algorithms will be applied for the sampling of parameter type definitions in order to obtain force fields with high accuracy while at the same time having as few types as necessary (thus being as simple as possible). At any given step of the parameter sampling process, existing parameter types are either merged or split into new ones. Since the number of possible merging or splitting operations is vast, parameter types will be represented through quantum-level atomic features, thus enabling a computable physics-based description for a given chemical environment. The significance of the proposed work is its fundamentally data-driven and rigorous way to build force fields without the restriction to a particular functional form or application domain of the force field. Furthermore, the developed approach will make force fields easily extensible if new reference data becomes available- an important aspect in materials design and drug discovery. Finally, the impact of the research will be maximized by implementing the developed technology into an open source python package.

分子过程是复杂的，只有一小部分细节是通过实验技术辨别出来的。然而，在许多应用中，能够预测相关的分子细节是非常重要的。在这种背景下，原子模拟已经变得越来越重要，以探测（生物）分子的性质和相互作用。虽然这些模拟在理论上是合理的，但它们不一定准确，并且错误的关键来源是潜在的分子力学力场，该力场将给定的分子结构与原子力相关联。今天，改进力场的一个主要障碍是用于将原子归类以分配力场参数的方案缺乏严谨性。这些类别通常被称为参数类型，将类似的化学环境（即子结构）分组，并将一组共同的参数分配给这些化学环境中的原子。为了避免过拟合和促进参数优化，这些类型应该尽可能少，同时仍然能够实现计算和参考（实验或高级量子化学计算）分子性质之间的良好一致性。然而，参数类型在历史上主要是以特别的方式分配的。这阻止了随着新的参考数据变得可用而对力场参数进行严格优化，以及直接将新的化学子结构引入现有力场。在这里，我提出了一种新的方法，通过结合数据驱动的力场参数类型定义和力场参数值的优化，克服了上述障碍。该方法与现有的力场优化方法有根本不同，现有的力场优化方法仅对给定的力场进行调整或添加参数。在拟议的项目中，贝叶斯推断和蒙特卡罗抽样算法将用于参数类型定义的抽样，以获得高精度的力场，同时具有尽可能少的类型（因此尽可能简单）。在参数采样过程的任何给定步骤中，现有参数类型要么被合并，要么被拆分为新的参数类型。由于可能的合并或分裂操作的数量是巨大的，参数类型将通过量子级原子特征来表示，从而为给定的化学环境提供基于可计算物理的描述。所提出的工作的意义在于其基本上是数据驱动和严格的方式来构建力场，而不限于力场的特定功能形式或应用领域。此外，如果新的参考数据可用，所开发的方法将使力场易于扩展-这是材料设计和药物发现的一个重要方面。最后，通过将开发的技术实施到开源python包中，将研究的影响最大化。