Abstract
Artificial Intelligence (AI) has emerged as a powerful tool in modern chemical research, significantly accelerating the discovery and development of new drugs and advanced materials. Traditional experimental approaches in chemistry are often time-consuming, expensive, and require extensive trial-and-error processes. AI techniques, particularly machine learning and deep learning, enable researchers to analyse large chemical datasets, predict molecular properties, and identify potential compounds with greater efficiency and accuracy. In drug discovery, AI helps in predicting drug–target interactions, optimizing molecular structures, and reducing the time required for lead identification. Similarly, in materials science, AI assists in designing novel materials with desired properties for applications in energy storage, catalysis, and electronics. This paper highlights the role of AI-driven computational models in transforming chemical research, discusses key methodologies, and examines current challenges and future prospects. The integration of artificial intelligence with chemical sciences is expected to revolutionize drug development and materials innovation, making the discovery process faster, more cost-effective, and highly efficient.