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RESEARCH PAPER · Angewandte Chemie International Edition · 2018

Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors

Wiktor Beker, Ewa P. Gajewska, Tomasz Badowski, Bartosz A. Grzybowski

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Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful DescriptorsOriginalUndiagnosed periodontal disease. A mock trial.0Computational design of syntheses leading to compound libraries or isotopically labelled targets29Selection of cost-effective yet chemically diverse pathways from the networks of computer-generated retrosynthetic plans75The logic of translating chemical knowledge into machine-processable forms: a modern playground for physical-organic chemistry43Automatic mapping of atoms across both simple and complex chemical reactions94Synthetic organic chemistry driven by artificial intelligence303Rapid and Accurate Prediction of pKa Values of C–H Acids Using Graph Convolutional Neural Networks91The art of atom descriptor design13Computer-generated “synthetic contingency” plans at times of logistics and supply problems: scenarios for hydroxychloroquine and remdesivir20From desktop to benchtop - Developing computational tools for organic and medicinal chemistry0Heck reaction prediction using a transformer model based on a transfer learning strategy52Machine learning the ropes: principles, applications and directions in synthetic chemistry279Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki–Miyaura cross-coupling reaction52A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation131Predictive Multivariate Models for Bioorthogonal Inverse-Electron Demand Diels–Alder Reactions54Quantum Chemical Reaction Prediction Method Based on Machine Learning24A Structure-Based Platform for Predicting Chemical Reactivity288Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning130Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning24Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, and Resins with Machine Learning215An efficient synthesis of strained thio-bridged compounds via Pd(0) catalyzed intramolecular Csp2(aryl)-Csp3(alkyl) cross dehydrohalogenative coupling reaction9From desktop to benchtop with automated computational workflows for computer-aided design in asymmetric catalysis59Exploring Reactivity and Regioselectivity of Dimerization of Paramagnetic Endohedral Metallofullerenes5Molecular Machine Learning: The Future of Synthetic Chemistry?72Molekulares maschinelles Lernen: Die Zukunft der Synthesechemie?3Computational planning of the synthesis of complex natural products271A Novel Data-Driven Method to Estimate Methane Adsorption Isotherm on Coals Using the Gradient Boosting Decision Tree: A Case Study in the Qinshui Basin, China28Accelerated 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 energies204Engines of discovery: Computers in advanced synthesis planning and identification of drug candidates0Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine189Unsupervised Assisted Directional Design of Chemical Reactions6Defining and Exploring Chemical Spaces124Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors169Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes74DeepReac+: deep active learning for quantitative modeling of organic chemical reactions52Machine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells70Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids27Machine learning prediction of hydrogen atom transfer reactivity in photoredox-mediated C–H functionalization33Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts105Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions91Chemist Ex Machina: Advanced Synthesis Planning by Computers47Data-driven Modeling of the Methane Adsorption Isotherm on Coal Using Supervised Learning Methods: A Comparative Study4Organic reactivity from mechanism to machine learning175Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions42Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions15An optimized deep convolutional neural network for yield prediction of Buchwald-Hartwig amination5Femtosecond dynamics and coherence of ionic retro-Diels–Alder reactions10Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning78Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning6National Institutes of Health (NIH) Workshop on 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Laboratories5Efficiency-optimized Diels-Alder reactions based on random forest1Data-Efficient, Chemistry-Aware Machine Learning Predictions of Diels–Alder Reaction Outcomes34Developing deep learning-based large-scale organic reaction classification model via sigma-profiles3AI for organic and polymer synthesis36Harnessing Electro-Descriptors for Mechanistic and Machine Learning Analysis of Photocatalytic Organic Reactions21When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?786‐Hydroxy Picolinohydrazides Promoted Cu(I)‐Catalyzed Hydroxylation Reaction in Water: Machine‐Learning Accelerated Ligands Design and Reaction Optimization46‐Hydroxy Picolinohydrazides Promoted Cu(I)‐Catalyzed Hydroxylation Reaction in Water: Machine‐Learning Accelerated Ligands Design and Reaction Optimization24Machine learning-guided strategies for reaction conditions design and optimization49Alkylation process intensification0Computational tools for the prediction of 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Machine Learning-Guided Study of Structure–Reactivity Relationships in Diels–Alder Cycloadditions1Automated DFT-Machine Learning Integration Enables Data-Efficient and Generalizable Feasibility Predictions in Metallaphotoredox sp 2 –sp 3 Cross-Coupling Reactions2Staged Diversity‐Constrained Machine Learning for High‐Dimensional Reaction Condition Optimization3Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning3Robots and Minimal, Physics‐Informed Features: A Hybrid Framework for Enzyme Catalysis0

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