# Computational models for the classification of mPGES-1 inhibitors with fingerprint descriptors

Computational models for the classification of mPGES-1 inhibitors with fingerprint descriptors Human microsomal prostaglandin $$\hbox {E}_{2}$$ E 2 synthase (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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Molecular Diversity Springer Journals

# Computational models for the classification of mPGES-1 inhibitors with fingerprint descriptors

, Volume 21 (3) – May 8, 2017
15 pages

/lp/springer_journal/computational-models-for-the-classification-of-mpges-1-inhibitors-with-cNh1gq6ywU
Publisher
Springer International Publishing
Subject
Life Sciences; Biochemistry, general; Organic Chemistry; Polymer Sciences; Pharmacy
ISSN
1381-1991
eISSN
1573-501X
D.O.I.
10.1007/s11030-017-9743-x
Publisher site
See Article on Publisher Site

### Abstract

Human microsomal prostaglandin $$\hbox {E}_{2}$$ E 2 synthase (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.

### Journal

Molecular DiversitySpringer Journals

Published: May 8, 2017

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