Integrated System of Swarm Intelligence and Neural Network for Molecular Similarity Detection

Authors

  • Fadia Sami Department of Computer Engineering, Information Technologies, Altınbaş University, Istanbul, Turkey https://orcid.org/0009-0001-6678-3859
  • Hakan KOYUNCU Department of Computer Engineering, Faculty of Engineering and Architecture, Altınbaş University, Istanbul, Turkey

DOI:

https://doi.org/10.21123/bsj.2024.9278

Keywords:

Molecular Similarity, Swarm intelligence (SI), Aquila, Termite, Neural Network

Abstract

Molecular similarity, governed by the principle that "similar molecules exhibit similar properties," is a pervasive concept in chemistry with profound implications, notably in pharmaceutical research where it informs structure-activity relationships. This study focuses on the pivotal role of molecular similarity techniques in identifying sample molecules akin to a target molecule while differing in key features. Within the realm of artificial intelligence, this paper introduces a novel hybrid system merging Swarm Intelligence (SI) behaviors (Aquila and Termites) with Neural Networks. Unlike previous applications where Aquila or Termites were used individually, this amalgamation represents a pioneering approach. The objective is to determine the most similar sample molecule in a dataset to a specific target molecule. Accuracy assessments reveal a manual evaluation accuracy of 70.58%, surging to 90% with the incorporation of Neural Networks. Additionally, a three-dimensional grid elucidates the Quantitative Structure-Activity Relationship (QSAR). The Euclidean and Manhattan Distance metrics quantify differences between molecules. This study contributes to molecular similarity assessment by presenting a hybrid approach that enhances accuracy in identifying similar molecules within complex datasets.

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Integrated System of Swarm Intelligence and Neural Network for Molecular Similarity Detection. Baghdad Sci.J [Internet]. [cited 2024 Dec. 6];22(2). Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9278