In recent years, data-driven approaches, such as data science (DS), artificial intelligence (AI), and big data, have been changing the way science is done because classical experiments, even the hyphenated or high-throughput approach, can be time-consuming, expensive, and dependent on the availability of equipment and reagents. Therefore, the use of tools and techniques related to DS, ML, and rational designs has attracted the attention of chemists because it can save valuable resources and time. In this sense, DS approaches have recently expanded to accelerate the discovery of new materials, drugs, and synthetic substances and automated compound identification.