Generative Artificial Intelligence Transitions Pharmaceutical Development from Empirical Screening to Predictive...

The traditional paradigm of pharmaceutical research is characterized by substantial inefficiency, requiring extensive timelines and billions of dollars while suffering from high clinical attrition rates. The integration of generative artificial intelligence (AI) is driving a paradigm shift from empirical experimentation toward predictive, data-driven innovation. This review evaluates state-of-the-art applications of these technologies across the drug discovery and development pipeline. By analyzing multi-omics data streams, AI models can elucidate complex disease mechanisms and identify novel therapeutic targets. Deep generative architectures facilitate the algorithmic creation of novel molecular entities, enabling the design of therapeutics with complex polypharmacological profiles. Furthermore, AI is enhancing the clinical testing phase through large language models (LLMs) that improve patient enrollment and through synthetic control arms (SCAs) that provide computational alternatives to traditional placebo groups. Despite these advances, the scientific community must address inherent algorithmic biases stemming from demographic underrepresentation and mitigate the risks of data hallucinations. Ultimately, realizing the full translational potential of generative AI in precision medicine may require the widespread adoption of explainable AI (XAI) frameworks and rigorous data standards.