Sami, FadiaKoyuncu, Hakan2025-06-132025-06-132025Sami, F., & Koyuncu, H. (2025). Integrated System of Swarm Intelligence and Neural Network for Molecular Similarity Detection. Baghdad Science Journal, 22(1).2078-86652411-7986https://hdl.handle.net/20.500.12939/5785Molecular 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.eninfo:eu-repo/semantics/openAccessAquilaMolecular similarityNeural networkSwarm intelligence (SI)TermiteArticle10.21123/bsj.2024.92782212-s2.0-105001180381Q1WOS:001451259000025Q3