AI-Accelerated Discovery for Sustainable Agrochemical and Pharmaceutical Innovation: An Industry Perspective

Abstract

Sustainable chemical innovation is increasingly constrained not by a shortage of green synthetic methods but by the speed and predictability with which discovery science translates candidate molecules into safe, scalable, and environmentally sound products. This Perspective, informed by more than two decades of industrial research and development leadership in agrochemical and pharmaceutical discovery, argues that artificial intelligence (AI)-enabled discovery has become the practical mechanism through which green chemistry principles and environmental, social, and governance (ESG) commitments are increasingly realized inside industrial research and development, rather than parallel initiatives pursued independently of one another. The evolution of AI in chemistry, from early quantitative structure-activity relationships to modern generative and structure-prediction models, is reviewed alongside its integration with green chemistry metrics, cheminformatics-guided agrochemical discovery, and responsible AI governance. To make these arguments actionable for R&D leaders, a conceptual framework, AIMS (AI-guided design, Integrated sustainability metrics, Model governance, Scale-up feedback loops), is proposed to structure the embedding of AI-enabled sustainability commitments across the discovery-to-manufacturing pipeline rather than retrofitting them at the end. A reflection on the discovery of an indigenous crop-protection molecule illustrates where AI-assisted approaches could compress discovery timelines and material intensity if applied today. The Perspective concludes with recommendations for industry, academic, and regulatory stakeholders embedding AI-enabled discovery within, rather than alongside, sustainable innovation governance.