Generative artificial intelligence in pharmaceutical drug development: A systematic review of time and cost efficiency across discovery, preclinical, and clinical phases
Aaron Riemer, Victoria Freund · Intelligent Pharmacy · 2026
The pharmaceutical drug development process is notoriously complex, lengthy, and costly, often requiring more than a decade and billions of dollars to bring a single compound to market. Generative artificial intelligence (AI) has emerged as a disruptive technology with the potential to accelerate this process by designing novel molecules, predicting biological interactions, and generating synthetic data. This systematic review, conducted according to PRISMA 2020 guidelines, analyzes 100 peer-reviewed studies published between 2018 and early 2025 to examine how generative AI contributes to time and cost efficiency across the discovery, preclinical, and clinical phases of drug development. The findings reveal that AI demonstrates the greatest reported impact in early-stage discovery, significantly reducing timelines for molecule identification and lead optimization while lowering experimental costs, although much of this evidence is based on in-silico proof-of-concept studies. In preclinical development, AI shows potential for predictive toxicology and resource-efficient study design, though applications remain limited and prospective validation is sparse. Clinical adoption is still emerging, with efficiency gains primarily seen in cost reduction rather than time savings, and no robust evidence yet that computational acceleration translates into reduced attrition or regulatory progress. Overall, the review highlights hidden costs as well as methodological, regulatory, and ethical barriers that currently hinder broader adoption. Future research should focus on expanding AI applications to later stages of development, standardizing evaluation metrics, and ensuring responsible, explainable integration into pharmaceutical R&D workflows.