Artificial Intelligence and Machine Learning in Drug Discovery: Evaluating Current Applications and Future Potential in Pharmaceuticals

Abstract

The application of AI and ML in drug discovery has significantly transformed the pharmaceutical sector. This integration has resulted in increased effectiveness, lower costs, and higher success rates across several stages of drug development. This research explores the current use of AI and ML, with a focus on their prospective applications in target identification, drug design acceleration, medication repurposing, clinical trial improvement, and personalized medicine development. In the study, which thoroughly investigates both quantitative and qualitative data, time to market (60–66% less time for total timeframes, for example) and cost (40–80% less for different R&D phases) are both dramatically decreased. Furthermore, AI can increase clinical trial success rates by up to 25%, which emphasizes how important it is for enabling the quick approval of novel treatments. Pharmaceutical companies may be better equipped to negotiate the intricacies of drug discovery and lay the groundwork for the production of tailored and targeted treatments that cater to the specific needs of individual patients by utilizing cutting-edge AI technologies and big data analytics. Artificial Intelligence (AI) and Machine Learning (ML) hold significant promise for the future of pharmaceutical research, as these technologies have the ability to revolutionize the pharmaceutical industry's drug manufacture and marketing.

Keywords: Artificial Intelligence, Machine Learning, Drug Discovery, Current Applications, Future of Pharma, Time to Market.