Machine learning is rapidly reshaping the field of chemical synthesis, influencing how chemists design, plan, and carry out reactions. Yet for many students, the role of artificial intelligence in synthesis remains abstract or inaccessible. This article introduces the current landscape of machine learning in chemistry, focusing on why these new approaches are emerging and how they complement, rather than replace, human creativity. We outline the challenges of traditional retrosynthetic planning, describe the rise of AI-driven platforms such as IBM RXN and AiZynthFinder, and discuss how they exemplify broader changes in the practice of chemistry. Practical “try it yourself” suggestions are included to help students and educators explore these developments firsthand. By highlighting both opportunities and limitations, this guide equips learners to critically understand the future of synthesis as a collaboration between chemists and computational tools.
Samrat Chakraborty (Thu,) studied this question.