Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning

Nasser, Maged and Salim, Naomie and Saeed, Faisal and Basurra, Shadi and Rabiu, Idris and Hentabli, Hamza and Alsoufi, Muaadh (2022) Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning. Biomolecules, 12 (4). ISSN 2218-273X

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The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve function-ally similar molecules. The most used conventional similarity methods used 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 performance of similarity searching methods. Thus, this study proposed a new approach for identifying the important features of molecules in chemical datasets based on the representation of the molecular features using Autoencoder (AE), with the aim of removing irrelevant and redundant features. The proposed approach experimented using the MDL Data Drug Report standard dataset (MDDR). Based on experimental findings, the proposed approach performed better than several existing benchmark similarity methods such as Tanimoto Similarity Method (TAN), Adapted Similarity Measure of Text Processing (ASMTP), and Quantum-Based Similarity Method (SQB). The results demonstrated that the performance achieved by the proposed approach has proven to be superior, particularly with the use of structurally heterogeneous datasets, where it yielded improved results compared to other previously used methods with the similar goal of improving molecular similarity searching.

Item Type: Article
Identification Number: https://doi.org/10.3390/biom12040508
22 March 2022Accepted
27 March 2022Published Online
Uncontrolled Keywords: molecular similarity; drug design; autoencoder; irrelevant and redundant features
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-03 - information systems
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Faisal Saeed
Date Deposited: 24 Mar 2022 11:37
Last Modified: 30 Mar 2022 15:46
URI: https://www.open-access.bcu.ac.uk/id/eprint/12977

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