Generative AI-Driven Drug Discovery Pipelines: Leveraging Cloud-Native Infrastructure for Accelerated Clinical Trials

Abstract

The integration of generative artificial intelligence into pharmaceutical drug discovery has emerged as one of the most consequential technological shifts in biomedical science over the past decade. This article presents findings from a multi-site observational study conducted across six pharmaceutical research institutions between 2023 and 2025, evaluating the impact of generative AI models and cloud-native computational infrastructure on the efficiency, cost, and success rate of drug discovery pipelines from lead identification through Phase II clinical trials. Across a cohort of 38 drug development programs spanning oncology, neurology, and infectious disease, we demonstrate that programs deploying large-scale generative molecular design models in conjunction with cloud-orchestrated data pipelines reduced median lead-to-candidate timelines by 41.3% (p < 0.001) and reduced computational costs per candidate by 62.7% relative to programs using conventional structure-based methods alone. We further characterize how cloud-native infrastructure, including containerized workloads, scalable genomic data lakes, and federated learning environments — enables previously intractable analyses such as real-time patient stratification and in-silico trial simulation. Our results provide empirical evidence that generative AI is not merely an adjunct to existing discovery methodology but represents a fundamental restructuring of how pharmaceutical candidates are designed, optimized, and validated. These findings carry significant implications for regulatory frameworks, data governance policy, and the economics of drug development globally.