Faculty of Physics, M. V. Lomonosov Moscow State University, Leninskie Gory, 119991 Moscow, Russia.
J Comput Aided Mol Des. 2013 May;27(5):427-42. doi: 10.1007/s10822-013-9656-4. Epub 2013 May 30.
The continuous molecular fields (CMF) approach is based on the application of continuous functions for the description of molecular fields instead of finite sets of molecular descriptors (such as interaction energies computed at grid nodes) commonly used for this purpose. These functions can be encapsulated into kernels and combined with kernel-based machine learning algorithms to provide a variety of novel methods for building classification and regression structure-activity models, visualizing chemical datasets and conducting virtual screening. In this article, the CMF approach is applied to building 3D-QSAR models for 8 datasets through the use of five types of molecular fields (the electrostatic, steric, hydrophobic, hydrogen-bond acceptor and donor ones), the linear convolution molecular kernel with the contribution of each atom approximated with a single isotropic Gaussian function, and the kernel ridge regression data analysis technique. It is shown that the CMF approach even in this simplest form provides either comparable or enhanced predictive performance in comparison with state-of-the-art 3D-QSAR methods.
连续分子场（CMF）方法基于应用连续函数来描述分子场，而不是通常用于此目的的有限分子描述符集（例如在网格节点处计算的相互作用能）。这些函数可以封装到核函数中，并与基于核的机器学习算法结合使用，为构建分类和回归结构活性模型、可视化化学数据集和进行虚拟筛选提供各种新方法。在本文中，通过使用五种类型的分子场（静电场、立体场、疏水性场、氢键接受体和供体场）、具有单个各向同性高斯函数逼近每个原子贡献的线性卷积分子核以及核岭回归数据分析技术，将 CMF 方法应用于构建 8 个数据集的 3D-QSAR 模型。结果表明，与最先进的 3D-QSAR 方法相比，CMF 方法即使在这种最简单的形式下也提供了相当或增强的预测性能。
