Optimizing Drug Discovery: Molecular Docking with Glow-Worm Swarm Optimization

In this chapter, we investigate the application of the Glow-worm Swarm Optimization (GSO) algorithm in molecular docking, utilizing the 3 × 29 dataset after thorough preprocessing and cleaning. We delve into the principles of protein–ligand interactions and introduce the GSO algorithm, inspired by glow-worm behavior, highlighting its core mechanisms encompassing movement, light intensity, and attraction–repulsion. The implementation of GSO for molecular docking involves fine-tuning its parameters to optimize performance, and we present a detailed performance evaluation, benchmarking GSO against traditional algorithms. Through computational techniques and scoring functions, we analyze docking accuracy, convergence, and efficiency. Additionally, we explore the integration of machine learning models for enhanced accuracy, compare machine learning and classical docking approaches, and identify potential drug candidates based on the outcomes. Real- world applications of molecular docking using the 3 × 29 dataset, such as structure-based drug design and virtual screening, are showcased. The chapter concludes with insights into future directions and challenges, considering advancements in the field, integration with artificial intelligence and deep learning, and addressing dataset bias and generalization, showcasing GSO as a valuable optimization algorithm with potential applications in drug discovery and medical research.