Exploring AI-driven approaches to drug discovery and development

Abstract

The integration of artificial intelligence (AI) into drug discovery and development has ushered in a transformative era in pharmaceutical research. The research explores the profound impact of AI-driven approaches in drug discovery and development, demonstrating, that computational intelligence and biomedicine synergize to enhance innovation, efficiency, and precision in pharmaceutical science. AI’s influence spans multiple phases of drug development, from target identification and validation to the optimization of drug candidates, while also facilitating personalized medicine and expediting drug repurposing. Recent studies underscore the precision and swiftness that AI brings to the discovery of drug candidates and the prediction of molecular properties, illustrating the potential advantages of AI in pharmaceutical research. However, AI’s application in healthcare demands cautious consideration, as concerns such as model interpretability, ethical data usage, and regulatory frameworks loom large. The research also the critical need for ethical and secure data utilization. It investigates the methodology employed to create data visualizations that offer comprehensive insights into the advantages and disadvantages of AI algorithms in drug discovery. The analysis emphasizes that a judicious and context-specific approach to AI algorithm selection is essential to harness the transformative power of AI while mitigating its limitations.