Computational models for the classification of mPGES-1 inhibitors with fingerprint descriptors
Computational models for the classification of mPGES-1 inhibitors with fingerprint descriptors
复制标题
使用指纹描述符对 mPGES-1 抑制剂进行分类的计算模型
DOI：
10.1007/s11030-017-9743-x
复制
发表时间：
2017
影响因子：
3.8
通讯作者：
Yan Aixia
中科院分区：
文献类型：
--
作者：
Xia Zhonghua;Yan Aixia
人微粒体前列腺素合成酶(mPGES)-1是治疗炎症和其他炎症症状疾病的一个有前景的药物靶点。在这项工作中，我们建立了分类模型，能够将mPGES-1抑制剂分为两组：高活性抑制剂和弱活性抑制剂。通过Kohonen自组织映射和随机选择两种方法，将1910个mPGES-1抑制剂数据集分为训练集和测试集。不同类型的指纹描述符包括MACCS键（MACCS）指纹、CDK指纹、Estate指纹、PubChem指纹、子结构指纹和二维原子对指纹。首先，我们使用支持向量机（SVM）构建了包含6种指纹的12个模型，发现MACCS在建模方面比其他指纹有一定的优势。接下来，我们使用naïve贝叶斯（NB）、随机森林（RF）和多层感知器（MLP）方法构建了仅使用MACCS的6个模型，发现使用RF和MLP方法的模型优于NB。最后，使用所有具有MACCS键的模型对41种化合物的外部测试集进行预测。综上所述，使用MACCS键和SVM、RF和MLP方法构建的模型对测试集和外部测试集都具有良好的预测性能。此外，基于指纹图谱的信息增益，我们对mPGES-1及其抑制剂进行了构效关系分析，确定了影响mPGES-1活性的关键官能团。研究发现，高活性抑制剂通常含有一个酰胺基团、一个芳环或一个氮杂环，以及几个杂原子取代基，如氟和氯。羧基和硫原子基主要出现在弱活性抑制剂中。
Human microsomal prostaglandinsynthase (mPGES)-1 is a promising drug target for inflammation and other diseases with inflammatory symptoms. In this work, we built classification models which were able to classify mPGES-1 inhibitors into two groups: highly active inhibitors and weakly active inhibitors. A dataset of 1910 mPGES-1 inhibitors was separated into a training set and a test set by two methods, by a Kohonen’s self-organizing map or by random selection. The molecules were represented by different types of fingerprint descriptors including MACCS keys (MACCS), CDK fingerprints, Estate fingerprints, PubChem fingerprints, substructure fingerprints and 2D atom pairs fingerprint. First, we used a support vector machine (SVM) to build twelve models with six types of fingerprints and found that MACCS had some advantage over the other fingerprints in modeling. Next, we used naïve Bayes (NB), random forest (RF) and multilayer perceptron (MLP) methods to build six models with MACCS only and found that models using RF and MLP methods were better than NB. Finally, all the models with MACCS keys were used to make predictions on an external test set of 41 compounds. In summary, the models built with MACCS keys and using SVM, RF and MLP methods show good prediction performance on the test sets and the external test set. Furthermore, we made a structure–activity relationship analysis between mPGES-1 and its inhibitors based on the information gain of fingerprints and could pinpoint some key functional groups for mPGES-1 activity. It was found that highly active inhibitors usually contained an amide group, an aromatic ring or a nitrogen heterocyclic ring, and several heteroatoms substituents such as fluorine and chlorine. The carboxyl group and sulfur atom groups mainly appeared in weakly active inhibitors.