Machine Learning for Synthetic Organic Chemistry: Methods, Applications, and Best Practices
TL;DR
The prerequisites for successfully applying data science in synthetic chemistry are outlined, and data‐driven approaches that can be applied in the development of new chemical reactions and synthetic methodologies are highlighted, including all relevant stages from reaction discovery and optimization to substrate scope evaluation and mechanistic analyses.
Abstract
The growing integration of artificial intelligence (AI) and machine learning (ML) is transforming experimental chemistry laboratories. Especially in synthetic chemistry, researchers routinely handle complex and high‐dimensional data, fostering meaningful synergies between chemistry and data science. This review is intended as a practical overview that connects the everyday challenges of synthetic chemists with the digital tools available to address them. It does not seek to explain theoretical foundations of ML or to provide a comprehensive survey of all recent studies in the field. Rather, our goal is to highlight emerging technologies, discuss key considerations for their application, and present a selection of illustrative examples. To begin, we outline the prerequisites for successfully applying data science in synthetic chemistry. Next, we give a realistic overview of strategies and bottlenecks in predictive modeling of molecular properties, reaction outcomes and reaction conditions. We further highlight data‐driven approaches that can be applied in the development of new chemical reactions and synthetic methodologies, including all relevant stages from reaction discovery and optimization to substrate scope evaluation and mechanistic analyses. Finally, we briefly discuss the transformative role of large language models and agentic workflows in synthetic chemistry, focusing on opportunities and challenges in the laboratories of the future.