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RESEARCH PAPER · Angewandte Chemie International Edition · 2020

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Xin Li, Shuo‐Qing Zhang, Li‐Cheng Xu, Xin Hong

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Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine LearningOriginalOptimizing chemical reaction conditions using deep learning: a case study for the Suzuki–Miyaura cross-coupling reaction52Organic Superbases in Recent Synthetic Methodology Research116Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N -Sulfonylimines toward Fast Multicomponent Reactions31Artificial intelligence and automation in computer aided synthesis planning88Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies204Regioselective synthesis of heterocyclic N-sulfonyl amidines from heteroaromatic thioamides and sulfonyl azides14Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors169DeepReac+: deep active learning for quantitative modeling of organic chemical reactions52Machine learning prediction of hydrogen atom transfer reactivity in photoredox-mediated C–H functionalization33Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts105Selectivity in organocatalysis—From qualitative to quantitative predictive models40Organic reactivity from mechanism to machine learning175Automation and computer-assisted planning for chemical synthesis189Predicting Reaction Yields via Supervised Learning187Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions42Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions15Prediction of Multicomponent Reaction Yields Using Machine Learning33Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning78Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning6Precise Introduction of the −CHnX3–n (X = F, Cl, Br, I) Moiety to Target Molecules by a Radical Strategy: A Theoretical and Experimental Study23Emerging Building Blocks for Medicinal Chemistry: Recent Synthetic Advances126Is Organic Chemistry Really Growing Exponentially?24Is Organic Chemistry Really Growing Exponentially?6Study of the Relationship between Synthesis Descriptors and the Type of Zeolite Phase Formed in ZSM‐43 Synthesis by Using Machine Learning6Origin and Regioselectivity of Direct Hydrogen Atom Transfer Mechanism of C(sp3)–H Arylation by [W10O32]4–/Ni Metallaphotoredox Catalysis21The roles of computer-aided drug synthesis in drug development29Seeking the Optimal Descriptor for S N 2 Reactions through Statistical Analysis of Density Functional Theory Results12Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction42Predicting reaction conditions from limited data through active transfer learning78RegioML: predicting the regioselectivity of electrophilic aromatic substitution reactions using machine learning24Mechanistically Guided Workflow for Relating Complex Reactive Site Topologies to Catalyst Performance in C–H Functionalization Reactions40Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability93Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling189New Insights and Predictions into Complex Homogeneous Reactions Enabled by Computational Chemistry in Synergy with Experiments: Isotopes and Mechanisms37An Ensemble Structure and Physicochemical (SPOC) Descriptor for Machine‐Learning Prediction of Chemical Reaction and Molecular Properties35Machine Learning Prediction of Structure‐Performance Relationship in Organic Synthesis16Mechanistic Inference from Statistical Models at Different Data-Size Regimes37When machine learning meets molecular synthesis56Auto Machine Learning Assisted Preparation of Carboxylic Acid by TEMPO‐Catalyzed Primary 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