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Aspirin, one of the most widely produced and consumed pharmaceuticals globally, presents an ideal case for exploring sustainable synthesis methods in pharmaceutical manufacturing. The conventional synthesis of aspirin, though widely practiced, often suffers from inefficiencies, excessive waste generation, and environmental concerns. This study presents a graph-theoretical framework for optimizing the aspirin synthesis pathway with a quantifiable alignment to green chemistry principles. A directed weighted graph was constructed, representing chemical species as nodes and reactions as edges, with weights derived from empirical reaction energy barriers and environmental impact metrics. Dynamic optimization algorithms, including an adaptation of the Bellman-Ford algorithm and cycle basis analysis, were employed to minimize total energy consumption and waste output. The proposed model achieved a 92% product yield, reduced waste generation by 63% (from 2.2 to 0.8 kg of waste per kg of aspirin), and lowered the energy requirement by 40% compared to the traditional synthesis process. The reaction efficiency index improved by 18.28%, while the average environmental impact factor decreased by 60.32%, validating the environmental and operational benefits of the approach. Although demonstrated using the aspirin pathway, the framework’s modular structure and reliance on quantifiable reaction parameters suggest its potential adaptability to other pharmaceutical syntheses, provided sufficient reaction data are available. This research not only offers a rigorous pathway optimization method but also bridges computational graph theory with industrial green chemistry applications.

Graph-theoretical optimization of the aspirin synthesis pathway: Enhancing green chemistry in pharmaceutical manufacturing