Quantum similarity: An Enhanced Framework using AUG Weighting Technique

Mohammed Mumtaz  Al-Dabbagh

doi:10.6180/jase.202501_28(1).0018

Quantum similarity: An Enhanced Framework using AUG Weighting Technique

Computer Science and Information Engineering

Mohammed Mumtaz Al-DabbaghThis email address is being protected from spambots. You need JavaScript enabled to view it.

Computer Engineering Department, Tishk International University, Erbil, Iraq

Received: December 7, 2023
Accepted: February 23, 2024
Publication Date: April 4, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202501_28(1).0018

In drug discovery, Virtual Screening (VS) encompasses the computational endeavor to unearth novel lead compounds via molecular similarity analysis. Amongst the array of techniques for ligand-based virtual screening (LBVS), similarity searching emerges as a quintessential and widely-adopted method. A prevailing assumption in many similarity search strategies posits that molecular structural features, irrespective of their biological activity, hold comparable importance. This paper delves into the AUG weighting scheme, aiming to bolster the application of quantum theory in LBVS, culminating in the formulation of a novel quantum-based similarity approach termed the QM-AUG method. Within the domain of molecular structure representation, the role of mathematical quantum space in enhancing the potency of the similarity method cannot be understated. The AUG weighting technique scrutinizes the potential consequences of adjusting weights allotted to chemical fragments, with the overarching objective of refining the quantum model’s efficiency in LBVS. Methodological robustness was gauged through the recall metrics of extracted active molecules, notably within the top 1% and 5% echelons. Furthermore, comprehensive experimental evaluations using authentic datasets, specifically the MDL Drug Data Report (MDDR) and Maximum Unbiased Validation (MUV), indicate that the proposed method surpasses the performance seen with its implementation in the Bayesian Inference Network and the conventional Taninmoto coefficient.

Keywords: ligand-based; Virtual screening; Quantum-based similarity; Similarity searching method; Quantum Weighting Scheme; AUG Weighting Technique

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