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Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning. | LitMetric

AI Article Synopsis

  • The study focuses on improving molecular similarity methods in drug design by addressing the limitations of conventional 2D fingerprints that often contain irrelevant and redundant features.
  • It proposes a new approach that uses Autoencoder (AE) technology to identify and retain only important molecular features, enhancing the efficiency of similarity searching.
  • Experimental results on the MDL Data Drug Report standard dataset (MDDR) showed that this new method outperformed traditional methods like Tanimoto and Quantum-Based Similarity in identifying similar molecules, especially in diverse datasets.

Article Abstract

The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve functionally 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9029813PMC
http://dx.doi.org/10.3390/biom12040508DOI Listing

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