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Efficient DNA-ligand interaction framework using fuzzy C-means clustering based glowworm swarm optimization (FCMGSO)

E Kiruba Nesamalar¹, J SatheeshKumar², T Amudha²

¹Research & Development Centre, Bharathiar University, Coimbatore, India.

Journal of Biomolecular Structure & Dynamics

|August 5, 2022

Summary

This summary is machine-generated.

Accurately mapping DNA and ligand interactions is crucial for drug discovery. This study introduces an optimized Fuzzy C-Means Clustering with Glowworm Swarm (FCMGSO) method, showing superior performance in predicting DNA-ligand binding energy compared to existing approaches.

Keywords:

DNA bio-inspired docking mining molecule

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Area of Science:

Computational Biology
Bioinformatics
Drug Discovery

Background:

Accurate DNA-ligand interaction mapping is vital for drug development and disease treatment.
Existing computational methods for predicting DNA-ligand binding energy often lack sufficient optimization, leading to suboptimal results.
The National Centre for Biotechnology Information (NCBI) dataset provides valuable instances for analyzing these interactions.

Purpose of the Study:

To develop an efficient and optimized model for predicting DNA-ligand mapping.
To improve the accuracy of binding energy prediction in DNA-ligand interactions.
To address the limitations of existing optimization methods in computational drug discovery.

Main Methods:

Application of established docking algorithms including Simulated Annealing (SA), Lamarckian Genetic Algorithm (LGA), Genetic Clustering (GC), Fuzzy C-means clustering (FCM), and Genetic Clustering with Multi swarm Optimization (GCMSO).
Development and implementation of a novel architecture using Fuzzy C-Means Clustering with Glowworm Swarm (FCMGSO) optimization.
Analysis of 500 DNA and drug instances from the NCBI dataset.

Main Results:

The proposed FCMGSO algorithm consistently demonstrated lower binding energy values across all analyzed samples compared to existing methods.
Existing algorithms like SA, LGA, GC, FCM, and GCMSO showed limitations in achieving optimal binding energy predictions.
The FCMGSO method proved more effective in optimizing the DNA-ligand docking process.

Conclusions:

The FCMGSO optimization method offers a significant advancement in accurately predicting DNA-ligand interactions.
This enhanced prediction accuracy can accelerate drug discovery and development for various diseases.
The study highlights the importance of advanced optimization techniques in computational drug design.