Molecular similarity searching based on deep learning for feature reduction

The concept of molecular similarity has been widely used in rational drug design, where structurally similar molecules are explored in molecular databases for retrieving functionally similar molecules. The most used conventional similarity methods are two-dimensional (2D) fingerprints to evaluate th...

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Main Author:	Saeed Nasser, Maged Mohammed
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/101478/1/MagedMohammedSaeedNasserPSC2022.pdf.pdf
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id	my-utm-ep.101478
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spelling	my-utm-ep.1014782023-06-21T10:09:44Z Molecular similarity searching based on deep learning for feature reduction 2022 Saeed Nasser, Maged Mohammed QA75 Electronic computers. Computer science The concept of molecular similarity has been widely used in rational drug design, where structurally similar molecules are explored in molecular databases for retrieving functionally similar molecules. The most used conventional similarity methods are two-dimensional (2D) fingerprints to evaluate the similarity of molecules towards a target query. However, these descriptors include redundant and irrelevant features that might impact the effectiveness of similarity searching methods. Moreover, the majority of existing similarity searching methods often disregard the importance of some features over others and assume all features are equally important. Thus, this study proposed three approaches for identifying the important features of molecules in chemical datasets. The first approach was based on the representation of the molecular features using Autoencoder (AE), which removes irrelevant and redundant features. The second approach was the feature selection model based on Deep Belief Networks (DBN), which are used to select only the important features. In this approach, the DBN is used to find subset of features that represent the important ones. The third approach was conducted to include descriptors that complement to each other. Different important features from many descriptors were filtered through DBN and combined to form a new descriptor used for molecular similarity searching. The proposed approaches were experimented on the MDL Data Drug Report standard dataset (MDDR). Based on the test results, the three proposed approaches overcame some of the existing benchmark similarity methods, such as Bayesian Inference Networks (BIN), Tanimoto Similarity Method (TAN), Adapted Similarity Measure of Text Processing (ASMTP) and Quantum-Based Similarity Method (SQB). The results showed that the performance of the three proposed approaches proved to be better in term of average recall values, especially with the use of structurally heterogeneous datasets that could produce results than other methods used previously to improve molecular similarity searching. 2022 Thesis http://eprints.utm.my/id/eprint/101478/ http://eprints.utm.my/id/eprint/101478/1/MagedMohammedSaeedNasserPSC2022.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150555 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Saeed Nasser, Maged Mohammed Molecular similarity searching based on deep learning for feature reduction
description	The concept of molecular similarity has been widely used in rational drug design, where structurally similar molecules are explored in molecular databases for retrieving functionally similar molecules. The most used conventional similarity methods are two-dimensional (2D) fingerprints to evaluate the similarity of molecules towards a target query. However, these descriptors include redundant and irrelevant features that might impact the effectiveness of similarity searching methods. Moreover, the majority of existing similarity searching methods often disregard the importance of some features over others and assume all features are equally important. Thus, this study proposed three approaches for identifying the important features of molecules in chemical datasets. The first approach was based on the representation of the molecular features using Autoencoder (AE), which removes irrelevant and redundant features. The second approach was the feature selection model based on Deep Belief Networks (DBN), which are used to select only the important features. In this approach, the DBN is used to find subset of features that represent the important ones. The third approach was conducted to include descriptors that complement to each other. Different important features from many descriptors were filtered through DBN and combined to form a new descriptor used for molecular similarity searching. The proposed approaches were experimented on the MDL Data Drug Report standard dataset (MDDR). Based on the test results, the three proposed approaches overcame some of the existing benchmark similarity methods, such as Bayesian Inference Networks (BIN), Tanimoto Similarity Method (TAN), Adapted Similarity Measure of Text Processing (ASMTP) and Quantum-Based Similarity Method (SQB). The results showed that the performance of the three proposed approaches proved to be better in term of average recall values, especially with the use of structurally heterogeneous datasets that could produce results than other methods used previously to improve molecular similarity searching.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Saeed Nasser, Maged Mohammed
author_facet	Saeed Nasser, Maged Mohammed
author_sort	Saeed Nasser, Maged Mohammed
title	Molecular similarity searching based on deep learning for feature reduction
title_short	Molecular similarity searching based on deep learning for feature reduction
title_full	Molecular similarity searching based on deep learning for feature reduction
title_fullStr	Molecular similarity searching based on deep learning for feature reduction
title_full_unstemmed	Molecular similarity searching based on deep learning for feature reduction
title_sort	molecular similarity searching based on deep learning for feature reduction
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Computing
publishDate	2022
url	http://eprints.utm.my/id/eprint/101478/1/MagedMohammedSaeedNasserPSC2022.pdf.pdf
_version_	1776100707479322624

Molecular similarity searching based on deep learning for feature reduction

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