Conformational analysis is of paramount importance in drug design: it is crucial to determine pharmacological properties, understand molecular recognition processes, and characterize the conformations of ligands when unbound. Molecular Mechanics (MM) simulation methods, such as Monte Carlo (MC) and molecular dynamics (MD), are usually employed to generate ensembles of structures due to their ability to extensively sample the conformational space of molecules. The accuracy of these MM-based schemes strongly depends on the functional form of the force field (FF) and its parametrization, components that often hinder their performance. High-level methods, such as ab initio MD, provide reliable structural information but are still too computationally expensive to allow for extensive sampling. Therefore, to overcome these limitations, we present a multilevel MC method that is capable of generating quantum configurational ensembles while keeping the computational cost at a minimum. We show that FF reparametrization is an efficient route to generate FFs that reproduce QM results more closely, which, in turn, can be used as low-cost models to achieve the gold standard QM accuracy. We demonstrate that the MC acceptance rate is strongly correlated with various phase space overlap measurements and that it constitutes a robust metric to evaluate the similarity between the MM and QM levels of theory. As a more advanced application, we present a self-parametrizing version of the algorithm, which combines sampling and FF parametrization in one scheme, and apply the methodology to generate the QM/MM distribution of a ligand in aqueous solution.

中文翻译：

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使用近似势生成量子配置系综

构象分析在药物设计中至关重要：确定药理学特性、了解分子识别过程以及表征未结合时的配体构象至关重要。分子力学 (MM) 模拟方法，例如蒙特卡罗 (MC) 和分子动力学 (MD)，通常用于生成结构的集合，因为它们能够广泛地对分子的构象空间进行采样。这些基于 MM 的方案的准确性在很大程度上取决于力场 (FF) 的函数形式及其参数化，这些组件通常会阻碍其性能。高级方法，例如ab initioMD，提供可靠的结构信息，但计算成本仍然太高，无法进行广泛的采样。因此，为了克服这些限制，我们提出了一种多级 MC 方法，该方法能够生成量子配置系综，同时将计算成本保持在最低水平。我们表明 FF 重新参数化是生成更紧密地再现 QM 结果的 FF 的有效途径，反过来，它可以用作低成本模型来实现黄金标准 QM 精度。我们证明了 MC 接受率与各种相空间重叠测量密切相关，并且它构成了评估 MM 和 QM 理论水平之间相似性的稳健指标。作为更高级的应用程序，我们提供了算法的自参数化版本，