Bioinformatics Computational Intelligence Approach of Polymeric-Coatings on Anti-Cancer drugs increasing their Longevity.

Abstract

Designing effective anti-cancer drugs is indeed a complex and challenging task due to various factors.The field of cancer treatment has witnessed significant advancements with the development of anti-cancer drugs; however, challenges such as limited drug half-life and systemic toxicity persist. This study explores a bioinformatics computational intelligence approach to address these challenges by employing polymeric coatings on anti-cancer drugs. The objective is to enhance drug stability, improve targeted delivery, and ultimately extend the drug's longevity within the biological system.

Despite the obstacles, scientists and researchers continue to make strides in this field, driven by the hope of improving cancer treatment outcomes and saving lives. Recently, Computational Intelligence a subset of artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs.

The research integrates bioinformatics tools and computational intelligence techniques to analyze molecular interactions between polymeric coatings and anti-cancer drugs. Molecular dynamics simulations, quantitative structure-activity relationship (QSAR) models, and machine learning algorithms are employed to predict the optimal polymeric coating for specific anti-cancer agents. This approach aims to identify coatings that not only enhance drug solubility and bioavailability but also mitigate adverse effects.