Generation of Pairwise Potentials Using Multidimensional Data Mining,Journal of Chemical Theory and Computation

当前位置： X-MOL 学术 › J. Chem. Theory Comput. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Generation of Pairwise Potentials Using Multidimensional Data Mining
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2018-09-05 00:00:00 , DOI: 10.1021/acs.jctc.8b00516
Zheng Zheng ₁ , Jun Pei ₁ , Nupur Bansal ₁ , Hao Liu ₁ , Lin Frank Song ₁ , Kenneth M. Merz ₁

Affiliation

The rapid development of molecular structural databases provides the chemistry community access to an enormous array of experimental data that can be used to build and validate computational models. Using radial distribution functions collected from experimentally available X-ray and NMR structures, a number of so-called statistical potentials have been developed over the years using the structural data mining strategy. These potentials have been developed within the context of the two-particle Kirkwood equation by extending its original use for isotropic monatomic systems to anisotropic biomolecular systems. However, the accuracy and the unclear physical meaning of statistical potentials have long formed the central arguments against such methods. In this work, we present a new approach to generate molecular energy functions using structural data mining. Instead of employing the Kirkwood equation and introducing the “reference state” approximation, we model the multidimensional probability distributions of the molecular system using graphical models and generate the target pairwise Boltzmann probabilities using the Bayesian field theory. Different from the current statistical potentials that mimic the “knowledge-based” PMF based on the 2-particle Kirkwood equation, the graphical-model-based structure-derived potential developed in this study focuses on the generation of lower-dimensional Boltzmann distributions of atoms through reduction of dimensionality. We have named this new scoring function GARF, and in this work we focus on the mathematical derivation of our novel approach followed by validation studies on its ability to predict protein–ligand interactions.

中文翻译：

使用多维数据挖掘生成成对电位

分子结构数据库的快速发展为化学界提供了访问大量可用于构建和验证计算模型的实验数据的途径。使用从实验可获得的X射线和NMR结构收集的径向分布函数，多年来使用结构数据挖掘策略已经开发了许多所谓的统计潜力。通过将其最初用于各向同性单原子系统扩展到各向异性生物分子系统，已在两粒子柯克伍德方程式的背景下开发了这些潜力。然而，统计潜力的准确性和不清楚的物理含义长期以来一直是反对这种方法的主要论点。在这项工作中，我们提出了一种使用结构数据挖掘生成分子能量函数的新方法。我们没有使用Kirkwood方程并引入“参考状态”近似，而是使用图形模型对分子系统的多维概率分布进行建模，并使用贝叶斯场论生成目标的成对Boltzmann概率。与目前的模拟基于2粒子Kirkwood方程的基于“知识”的PMF的统计势不同，本研究中开发的基于图形模型的基于结构的势专注于原子的低维Boltzmann分布的生成通过减少尺寸。我们将这个新的评分函数命名为GARF，

更新日期：2018-09-05

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11