Leveraging Artificial Intelligence for Sustainable Development of Azo Dye-Based Metal Complexes with Antimicrobial Properties

Abstract

Abstract The rapid development of artificial intelligence (AI) has created new opportunities for accelerating scientific research, particularly in materials science, medicinal chemistry, coordination chemistry, and sustainable chemical development. Azo dye-based metal complexes represent an important class of coordination compounds because of their diverse structural characteristics, tunable electronic properties, and potential biological activities. However, conventional synthesis and screening of such complexes can be time-consuming, resource-intensive, and dependent on extensive experimental trials. The integration of artificial intelligence and machine learning can provide an efficient approach for predicting molecular properties, optimizing synthesis conditions, and identifying compounds with enhanced antimicrobial potential. The present study proposes an AI-assisted framework for the sustainable development of azo dye-based metal complexes with antimicrobial properties. In this approach, molecular descriptors, structural characteristics, physicochemical properties, metal-ion characteristics, and experimentally determined antimicrobial data can be integrated into machine-learning models. These models can be trained to establish structure–activity relationships and predict the antimicrobial performance of newly designed complexes before their experimental synthesis. Such predictive modelling may help researchers prioritize promising compounds and reduce unnecessary laboratory experimentation. The proposed approach also supports the principles of green and sustainable chemistry by potentially reducing chemical consumption, solvent use, energy requirements, experimental repetitions, and laboratory waste. AI can further assist in optimizing reaction parameters such as temperature, reaction time, solvent, pH, reactant ratio, and catalyst concentration to improve yield and efficiency. The integration of AI with coordination chemistry can therefore contribute to the development of safer, more efficient, and environmentally responsible antimicrobial materials. The approach is particularly relevant to India's emerging digital scientific ecosystem, where AI-enabled research can support innovation, reduce research costs, and strengthen interdisciplinary collaboration between chemistry, computational science, biotechnology, and data science. The study concludes that AI should be viewed as a complementary tool to experimental chemistry rather than a replacement for laboratory validation. Combining computational prediction with synthesis, characterization, and antimicrobial evaluation can provide a powerful pathway for developing novel azo dye-based metal complexes while supporting sustainable scientific development.