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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.

Keywords

Molecular similarity, Swarm intelligence (SI), Aquila, Termite, Neural network

Subject Area

Computer Science

First Page

280

Last Page

296

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Receive Date

8-14-2023

Revise Date

12-25-2023

Accept Date

12-27-2023

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