ACCELERATING PHARMACEUTICAL INNOVATION: THE IMPACT OF AI ON DRUG DISCOVERY
Keywords:
AI, Drug Discovery, Machine Learning, Deep Learning, Pharmaceutical Research, Virtual Screening, Molecular Docking, Generative ModelsAbstract
The integration of artificial intelligence (AI) and machine learning technologies in drug discovery has the potential to revolutionize the pharmaceutical industry. Traditional drug discovery processes are time-consuming, costly, and often inefficient, with the average drug development timeline spanning over a decade and costs exceeding billions of dollars. However, AI-powered approaches offer a transformative solution by efficiently analyzing vast amounts of data, predicting molecular interactions, and identifying promising drug candidates. This article explores the various methodologies employed in AI-driven drug discovery, including deep learning models, virtual screening, molecular docking, and generative models. The results section talks about several successful case studies that show how AI can speed up the search for new therapeutic agents. For example, powerful inhibitors for cancer-related enzymes were found, and drug candidates for obsessive-compulsive disorder and antibiotic-resistant bacteria were made very quickly. Despite the promising results, challenges such as data quality, model interpretability, and experimental validation need to be addressed to fully realize the potential of AI in drug discovery.
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