Integrated System of Swarm Intelligence and Neural Network for Molecular Similarity Detection
DOI:
https://doi.org/10.21123/bsj.2024.9278Keywords:
Molecular Similarity, Swarm intelligence (SI), Aquila, Termite, Neural NetworkAbstract
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.
Received 15/08/2023
Revised 25/12/2023
Accepted 27/12/2023
Published Online First 20/07/2024
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