Machine Learning Prediction of Structure-Performance Relationship in Organic Synthesis

Comprehensive Summary Data-driven approach has emerged as a powerful strategy in the construction of structure-performance relationships in organic synthesis. To close the gap between mechanistic understanding and synthetic prediction, we have made efforts to implement mechanistic knowledge in machine learning modelling of organic transformation, as a way to achieve accurate predictions of reactivity, regio- and stereoselectivity. We have constructed a comprehensive and balanced computational database for target radical transformations (arene C-H functionalization and HAT reaction), which laid the foundation for the reactivity and selectivity prediction. Furthermore, we found that the combination of computational statistics and physical organic descriptors offers a practical solution to build machine learning structure-performance models for reactivity and regioselectivity. To allow machine learning modelling of stereoselectivity, a structured database of asymmetric hydrogenation of olefins was built, and we designed a chemical heuristics-based hierarchical learning approach to effectively use the big data in the early stage of catalysis screening. Our studies reflect a tiny portion of the exciting developments of machine learning in organic chemistry. The synergy between mechanistic knowledge and machine learning will continue to generate a strong momentum to push the limit of reaction performance prediction in organic chemistry. How do you get into this specific field? Could you please share some experiences with our readers? Based on my study experience in Prof. Houk's lab and Prof. Norskov's lab, my major idea since the beginning of my lab is to combine the key design principles of homogeneous catalysis (transition state model) and heterogeneous (scaling relationship) catalysis. This idea eventually evolved to our explorations of mechanism-based machine learning in organic chemistry. How do you supervise your students? I try my best to give them enough space and freedom, so they can experience the joy in chemistry research. What are your hobbies? I enjoy science fiction movies and novels. What is the most important personality for scientific research? Chemistry has unlimited frontiers. Targeting a hardcore question, developing someone's own approach is the most important merit in fundamental scientific research. How do you keep balance between research and family? Work-life balance is certainly one of the biggest challenges for junior faculty. I try to work in fragmented time, so I would be available for both my family and my students. Who influences you mostly in your life? My high-school experience in Chemistry Olympiad has influenced me dramatically, which cultivated my independent learning ability to tackle new questions. This has helped me a lot throughout my career.

Automated summary

Data-driven approaches have proven to be effective in bridging the gap between mechanistic understanding and synthetic prediction in organic synthesis. This article discusses the implementation of mechanistic knowledge in machine learning models for accurate predictions of reactivity and selectivity. The authors emphasize the importance of comprehensive computational databases and the combination of computational statistics and physical organic descriptors in building structure-performance models. They also highlight the significance of integrating mechanistic knowledge and machine learning in pushing the limits of reaction performance prediction in organic chemistry.