In recent years, asymmetric catalysis of ynamides has attracted much attention, but these reactions mostly constructed central chirality, except for a few examples on the synthesis of axially chiral compounds which exclusively relied on noble-metal catalysis. Herein, a facile access to axially chiral Nheterocycles enabled by chiral Brønsted acid-catalyzed 5-endo-dig cyclization of ynamides is disclosed, which represents the first metal-free protocol for the construction of axially chiral compounds from ynamides. This method allows the practical and atom-economical synthesis of valuable N-arylindoles in excellent yields with generally excellent enantioselectivities. Moreover, organocatalysts and ligands based on such axially chiral Narylindole skeletons are demonstrated to be applicable to asymmetric catalysis.

We discovered a cooperative gold/silver catalysis mechanism in the oxidative cross-coupling reaction between 1,2,4,5-tetrafluorobenzene and N-TIPS-indole, using DFT calculations. A silver(I)catalyzed CMD mechanism is responsible for the C−H activation of 1,2,4,5-tetrafluorobenzene, and C−H acidity determines the chemoselectivity. A gold(III)-catalyzed S E 2Ar mechanism is responsible for the C3−H activation of N-TIPS-indole, and arene nucleophilicity determines the chemo-and regioselectivity. The orthogonal chemoselectivity control provides a mechanistic guide for dual C−H activation reactions.

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. Nørskov'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.

In the past two decades, substantial advances have been made on the asymmetric alkyne functionalization by the activation of inert alkynes. However, these asymmetric transformations have so far been mostly limited to transition metal catalysis, and chiral Brønsted acid–catalyzed examples are rarely explored. Here, we report a chiral Brønsted acid–catalyzed dearomatization reaction of phenol- and indole-tethered homopropargyl amines, allowing the practical and atom-economical synthesis of a diverse array of valuable fused polycyclic enones and indolines bearing a chiral quaternary carbon stereocenter and two contiguous stereogenic centers in moderate to good yields with excellent diastereoselectivities and generally excellent enantioselectivities (up to >99% enantiomeric excess). This protocol demonstrates Brønsted acid–catalyzed asymmetric dearomatizations via vinylidene–quinone methides.