The most recent phase in the history of digital chemistry involves incorporating digital retrosynthesis tools for predicting reactions, machine learning (ML), and big data analytics. The explosion of chemical data available from literature, databases, and experimental results, combined with advanced ML algorithms, has led to unprecedented capabilities in predicting material properties, reaction outcomes, and even designing new molecules. The Human Genome Project, the over-exponential use of large language models, and the rise of Chemical Biology, for example, with Click Chemistry, also triggered the exploration of the chemical and process space, sometimes with the aid of supercomputers. Prelude: Future Perspective Digital chemistry is poised to become even more integrated with advancements in artificial intelligence, quantum computing, and laboratory automation technologies, such as self-driving laboratories. These developments promise to revolutionize the way chemical research is conducted, making it faster, more efficient, more reliable in terms of quality, more reproducible, and more sustainable. Throughout its history, digital chemistry has continually transformed the landscape of chemical research, moving from simple computational models to complex systems capable of simulating and predicting intricate chemical phenomena. As computational power and algorithms evolve, digital chemistry stands at the forefront of the next generation of scientific discovery and innovation.