The continuous molecular fields approach to building 3D-QSAR models
The continuous molecular fields approach to building 3D-QSAR models
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DOI：
10.1007/s10822-013-9656-4
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发表时间：
2013-05-01
影响因子：
3.5
通讯作者：
Zhokhova, Nelly I.
中科院分区：
文献类型：
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作者：
Baskin, Igor I.;Zhokhova, Nelly I.
连续分子场（CMF）方法是基于连续函数的应用来描述分子场，而不是通常用于此目的的有限的分子描述符集（如在网格节点上计算的相互作用能量）。这些功能可以被封装到核中，并与基于核的机器学习算法相结合，为构建分类和回归结构-活性模型、可视化化学数据集以及进行虚拟筛选提供各种新颖的方法。本文利用五种类型的分子场（静电场、位阻场、疏水场、氢键受体场和供体场）、每个原子的贡献近似为单一各向同性高斯函数的线性卷积分子核以及核脊回归数据分析技术，应用CMF方法构建了8个数据集的3D-QSAR模型。研究表明，与最先进的3D-QSAR方法相比，即使在这种最简单的形式下，CMF方法也提供了相当或增强的预测性能。
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.