Artificial Intelligence in Chemistry: A Comprehensive Review of Methods, Applications, and Future Perspectives

Abstract

Artificial intelligence (AI) is increasingly reshaping the chemical sciences by supporting data-centric discovery, predictive simulations, and automated laboratory workflows. Contemporary AI approaches such as machine learning, deep neural networks, graph-based models, and natural language processing enable the systematic interpretation of intricate chemical datasets and molecular structures. These technologies have found extensive use in areas including pharmaceutical development, reaction forecasting, computer-assisted retrosynthesis, materials engineering, catalyst design, and spectral data analysis. The combination of AI with high-throughput screening techniques and robotic platforms has accelerated the emergence of autonomous, self-operating laboratories. Nevertheless, important limitations persist, particularly regarding data reliability, transparency of model decisions, reproducibility of results, and the adaptability of models across different chemical domains. Ongoing progress in algorithm development, dataset standardization, and collaborative interaction between human expertise and intelligent systems is anticipated to foster more sustainable, efficient, and innovative advancements in chemical research.