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The pharmaceutical industry faces mounting pressure to reduce its environmental impact while maintaining innovation in drug development. Artificial intelligence (AI) has emerged as a transformative tool across healthcare and drug discovery, yet its potential to drive sustainability by improving molecular design remains underexplored. This review critically examines the applications of AI in molecular design that can support in advancing greener pharmaceutical practices across the entire drug life cycle—from design and synthesis to waste management and solvent optimisation. We explore how AI-driven models are being used to personalise dosing, reduce pharmaceutical waste, and design biodegradable drugs with enhanced environmental compatibility. Significant advances have also been made in the predictive modelling of pharmacokinetics, drug–polymer interactions, and polymer biodegradability. AI’s role in the synthesis of active pharmaceutical compounds, including catalysts, enzymes, solvents, and synthesis pathways, is also examined. We highlight recent breakthroughs in protein engineering, biocatalyst stability, and heterogeneous catalyst screening using generative and language models. This review also explores opportunities and limitations in the field. Despite progress, several limitations constrain impact. Many AI models are trained on small or inconsistent datasets or rely on computationally intensive inputs that limit scalability. Moreover, a lack of standardised performance metrics and life cycle assessments prevents the robust evaluation of AI’s true environmental benefits. In particular, the environmental impact of AI-driven molecules and synthesis pathways remains poorly quantified due to limited data on emissions, waste, and energy usage at the compound level. Finally, a summary of challenges and future directions in the field is provided.
Antonio Ruiz-Gonzalez (Thu,) studied this question.