Artificial Intelligence-driven Drug Discovery: Transforming Pharmaceutical Innovation Through Computational Approaches

Abstract: The process of identifying and optimizing new drugs has long been recognized as expensive and complex. Many promising compounds have failed during the late stages of development, resulting in lost time and investment. Despite major advances in pharmaceutical sciences, challenges such as low efficacy, toxicity, and poor pharmacological properties remain significant roadblocks to innovation. Recently, the use of artificial intelligence (AI), advanced computing, and innovative laboratory technologies has revolutionized this process. By integrating AI models, predictive algorithms, and machine learning techniques into drug discovery, scientists can rapidly identify promising lead compounds, predict potential toxicities, optimize pharmacological properties, and accelerate the transition from the laboratory to the patient's bedside. Computational approaches are also being explored for repurposing existing drugs for new therapeutic applications, which may help reduce development costs and risks. Genomic therapies and precision medicine offer a new paradigm by tailoring treatments to individual patients to enhance efficacy and minimize toxicities. Collectively, these emerging approaches not only accelerate development but also improve the likelihood of producing drugs that are highly targeted, safe, bioavailable, and effective. This review examines the current literature on drug discovery and development, highlighting the role of AI and computational technologies in transforming the pharmaceutical and healthcare sectors. It also emphasizes the importance of drug repurposing, AI-assisted design, and genomic approaches in modern drug discovery for reducing trial costs and failure rates.