Sustainable Drug Discovery and Development: Integrating Green Synthetic Chemistry with Artificial Intelligence-Based Safety Screening

Abstract

The pharmaceutical sector must deliver safe, effective medicines while limiting the environmental burden of their discovery and manufacture. Green synthetic chemistry addresses this through waste prevention, atom economy, safer solvents, and catalysis, tracked using metrics such as the E-factor and process mass intensity1, 2. Artificial intelligence (AI) is now accelerating this shift: AI-guided retrosynthesis and reaction-prediction platforms design shorter, lower-waste synthetic routes, while generative models and machine-learning-guided directed evolution support greener solvents and biocatalysts16. In parallel, AI-driven ADMET prediction tools flag unsafe candidate early, reducing late-stage attrition and reliance on animal testing25. This review synthesises current literature on integrating green synthetic chemistry with AI-based safety screening across the drug discovery and development pipeline, covering sustainable synthetic strategies, AI-enabled route and biocatalyst optimisation, computational toxicology, representative platforms, and the regulatory landscape, alongside persistent challenges such as data quality, model interpretability, and the barriers to scaling green processes industrially. The review concludes that this integration offers a practical, increasingly necessary pathway toward medicines that are safer for patients and less burdensome for the planet.