AI-ASSISTED GREEN SYNTHESIS OF PHARMACEUTICAL COMPOUNDS: RECENT ADVANCES, APPLICATIONS, CHALLENGES AND FUTURE PERSPECTIVES

Pharmaceutical synthesis is a fundamental component of drug discovery and development, providing efficient routes for the preparation of active pharmaceutical ingredients and other therapeutically important compounds. However, conventional organic synthesis often relies on hazardous reagents, toxic solvents, excessive energy consumption, multi-step reactions, and generates substantial chemical waste, creating significant environmental, economic, and safety concerns. Green chemistry has emerged as an important approach to address these challenges by promoting the prevention of waste, improved atom economy, safer solvents and reagents, energy efficiency, catalysis, and the design of less hazardous chemical processes. The recent integration of artificial intelligence (AI) and machine learning (ML) with organic chemistry has created new opportunities to improve the sustainability and efficiency of pharmaceutical synthesis. AI- and ML-based models can analyse large chemical datasets and assist in predicting reaction outcomes, optimizing reaction conditions, selecting appropriate catalysts and solvents, and estimating reaction yields and selectivity. These approaches can reduce the number of experimental trials, consumption of chemicals, energy requirements, and overall waste generation during process development. Major applications include computer-assisted retrosynthetic analysis, reaction prediction, synthetic route optimization, catalyst and solvent selection, molecular and reaction-condition optimization, and AI-assisted automated synthesis of pharmaceutical compounds. The integration of AI with green chemistry can therefore support the development of shorter, safer, more economical, and environmentally sustainable synthetic routes for pharmaceuticals. Nevertheless, several challenges remain, including limited and biased datasets, data quality, model interpretability, prediction accuracy, computational requirements, experimental validation, scalability, and regulatory acceptance. Future developments are expected to focus on the integration of AI with automation, robotics, continuous-flow chemistry, real-time reaction monitoring, and sustainable-by-design approaches. This review highlights the role, applications, advantages, limitations, and future prospects of AI-assisted green synthesis in pharmaceutical chemistry.