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

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dc.contributor.authorSami, Fadia
dc.contributor.authorKoyuncu, Hakan
dc.date.accessioned2025-06-13T13:08:30Z
dc.date.available2025-06-13T13:08:30Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Bilişim Teknolojileri Ana Bilim Dalı
dc.description.abstractMolecular 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.
dc.identifier.citationSami, F., & Koyuncu, H. (2025). Integrated System of Swarm Intelligence and Neural Network for Molecular Similarity Detection. Baghdad Science Journal, 22(1).
dc.identifier.doi10.21123/bsj.2024.9278
dc.identifier.issn2078-8665
dc.identifier.issue1
dc.identifier.scopus2-s2.0-105001180381
dc.identifier.scopusqualityQ1
dc.identifier.uri2411-7986
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5785
dc.identifier.volume22
dc.identifier.wosWOS:001451259000025
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.institutionauthorSami, Fadia
dc.institutionauthorKoyuncu, Hakan
dc.language.isoen
dc.publisherColl Science Women
dc.relation.ispartofBaghdad Science Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrenci
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAquila
dc.subjectMolecular similarity
dc.subjectNeural network
dc.subjectSwarm intelligence (SI)
dc.subjectTermite
dc.titleIntegrated System of Swarm Intelligence and Neural Network for Molecular Similarity Detection
dc.typeArticle

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