Privacy-Preserving Data Pipelines for AI: A Comprehensive Review of Scalable Approaches
Prateik Mahendra, Alisha Verma · 2025
The pharmaceutical sector is confronted with compelling drug discovery and development challenges, high expenses and long timelines stifling innovation. Generative artificial intelligence (GenAI) holds the potential to revolutionize drug discovery by speeding up the discovery process, presenting new methods for molecule design, target identification, and candidate screening. Here, the authors distill recent progress in generative AI-driven applications for drug discovery, highlighting relevant architectural paradigms, implementation tactics, and leadership frameworks for integrating successfully. Based on ten landmark research papers released between 2021 and 2024, we examine how different generative models, such as generative adversarial networks (GANs), variational autoencoders (VAEs), transformer models, and diffusion models, are transforming pharmaceutical research. The review emphasizes the strategic leadership strategies needed to deploy these technologies successfully, with solutions to overcome challenges in cross-disciplinary collaboration, data governance, and ethics. By presenting an overall structure for the integration of generative AI into pharmaceutical research pipelines, this paper provides advice for research directors at the confluence of artificial intelligence and drug discovery seeking more efficient, less costly, and more innovative therapeutic development.