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RESEARCH PAPER · Angewandte Chemie International Edition · 2022

Machine Learning for Chemical Reactivity: The Importance of Failed Experiments

Felix Strieth‐Kalthoff, Frederik Sandfort, Marius Kühnemund, Felix R. Schäfer, Herbert Kuchen, Frank Glorius

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Machine Learning for Chemical Reactivity: The Importance of Failed ExperimentsOriginalA Conceptual Theory of Model-Science Interface: Where AI Outputs Become Experimental Inputs0Chemistry must respond to the crisis of transgression of planetary boundaries46Energy Transfer Photolysis of N-Enoxybenzotriazoles into Benzotriazolyl and α-Carbonyl Radicals27A special issue on artificial intelligence for drug discovery0Deep Learning for Additive Screening in Perovskite Light‐Emitting Diodes5Deep Learning for Additive Screening in Perovskite Light‐Emitting Diodes40Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings87Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab294Reaxtica: A knowledge-guided machine learning platform for fast and accurate reaction selectivity and yield prediction0The chemistry of errors9Reaxtica: A knowledge-guided machine learning platform for fast and accurate reaction selectivity and yield prediction1Roadmap to Pharmaceutically Relevant Reactivity Models Leveraging High-Throughput Experimentation17Machine learning for yield prediction for chemical reactions using in situ sensors11Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling208Controlled Synthesis of Multicolor Carbon Dots Assisted by Machine Learning71Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery138Machine learning for the yield prediction of CO2 cyclization reaction catalyzed by the ionic liquids21A focus on the use of real-world datasets for yield prediction3Autonomous optimization of an organic solar cell in a 4-dimensional parameter space50Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane15On the use of real-world datasets for reaction yield prediction141Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis1The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions39The rise of automated curiosity-driven discoveries in chemistry20Detours in the Development of the Organocatalytic Diels‐Alder Reactions of Vinylboronic Compounds. A Dead End?0ConvPred : A deep learning‐based framework for predictions of potential organic reactions8Machine Learning C–N Couplings: Obstacles for a General-Purpose Reaction Yield Prediction48Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation48Open data and algorithms for open science in AI-driven molecular informatics27The value of negative results in data-driven catalysis research72Science‐Driven Atomistic Machine Learning45Science‐Driven Atomistic Machine Learning3Small Data Can Play a Big Role in Chemical Discovery38Small Data Can Play a Big Role in Chemical Discovery7AiZynthTrain: Robust, Reproducible, and Extensible Pipelines for Training Synthesis Prediction Models30A Machine Learning Approach to Model Interaction Effects: Development and Application to Alcohol Deoxyfluorination23Functional Group Evaluation Kit for Digitalization of Information on the Functional Group Compatibility and Chemoselectivity of Organic Reactions32Negative Data in Data Sets for Machine Learning Training52Negative Data in Data Sets for Machine Learning Training48Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use37Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use6Asymmetric Synthesis of α‐Methylene‐γ‐Butyrolactones via Tandem Allylboration/Lactonization: a Kinetic Resolution Process17Asymmetric Synthesis of α‐Methylene‐γ‐Butyrolactones via Tandem Allylboration/Lactonization: a Kinetic Resolution Process2Recent applications of machine learning in alloy design: A review158Machine Learning Prediction of High-Yield Cobalt- and Nickel-Catalyzed Borylations10Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity2Extraction Yield Prediction for the Large-Scale Recovery of Botanicals1Machine Learning-Enabled Predictions of Condensed Fukui Functions and Designing of Metal Pincer Complexes for Catalytic Hydrogenation of CO29High‐Throughput Experimentation and Machine Learning‐Assisted Optimization of Iridium‐Catalyzed Cross‐Dimerization of Sulfoxonium Ylides49High‐Throughput Experimentation and Machine Learning‐Assisted Optimization of Iridium‐Catalyzed Cross‐Dimerization of Sulfoxonium Ylides9Revisiting the Paradigm of Reaction Optimization in Flow with a Priori Computational Reaction Intelligence12Revisiting the Paradigm of Reaction Optimization in Flow with a Priori Computational Reaction Intelligence2Machine learning approach for predicting the yield of pyrroles and dipyrromethanes condensation reactions with aldehydes13Deciphering melting behaviors of energetic compounds using interpretable Machine learning for melt-castable applications13Organopalladium Catalysis as a Proving Ground for Data-Rich Approaches to Reaction Development and Quantitative Predictions8Transfer learning for a foundational chemistry model41Extraction yield prediction for the large-scale recovery of cannabinoids6Boosting the generality of catalytic systems by the synergetic ligand effect in Pd-catalyzed C-N cross-coupling8Dataset Design for Building Models of Chemical Reactivity94When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges64Mechanistically guided development of homogenous nickel catalysis through rapid computational catalyst screening2Methylene Insertion into Nitrogen‐Heteroatom Single Bonds of 1,2‐Azoles via a Zinc Carbenoid: An Alternative Tool for Skeletal Editing17Investigating the Big Data Challenges of Deep Learning for Data Science0A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target32Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices28Cost-informed Bayesian reaction optimization17Every atom counts: predicting sites of reaction based on chemistry within two bonds0Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry11Improving reproducibility through condition-based sensitivity assessments: application, advancement and prospect46Machine Learning Assisted Prediction of the Mass Activity of Platinum-Based Anode Catalysts Supported on a Variety of Carbon Nanostructures for Direct Methanol Fuel Cells0One-pot synthesis of functionalized dihydropyridin-2-ones via carbene-catalyzed base-controlled [3+3] annulation reaction1Palladium-catalyzed decarboxylative (4 + 3) cycloadditions of bicyclobutanes with 2-alkylidenetrimethylene carbonates for the synthesis of 2-oxabicyclo[4.1.1]octanes65Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts26Probing the chemical ‘reactome’ with high-throughput experimentation data53Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis60Paving the road towards automated homogeneous catalyst design32Understanding Hot Injection Quantum Dot Synthesis Outcomes Using Automated High-Throughput Experiment Platforms and Machine Learning24FAIR Data and Software: Improving Efficiency and Quality of Biocatalytic Science13Expanding the Applicability Domain of Machine Learning Model for Advancements in Electrochemical Material Discovery7Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte143Continuum Robots and Magnetic Soft Robots: From Models to Interdisciplinary Challenges for Medical Applications24An evolutionary algorithm for interpretable molecular representations7Reaction Rebalancing: A Novel Approach to Curating Reaction Databases2Bridging the information gap in organic chemical reactions28Reaction Rebalancing: A Novel Approach to Curating Reaction Databases0Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality21Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge34Hydrodesulfurization of Dibenzothiophene: A Machine Learning Approach15From Synthesis Conditions to UiO-66 Properties: Machine Learning Approach15EnTdecker − A Machine Learning-Based Platform for Guiding Substrate Discovery in Energy Transfer Catalysis43Illuminating ‘the ugly side of science’: fresh incentives for reporting negative results19Information reuse of nondestructive evaluation (NDE) data sets8Incorporating Synthetic Accessibility in Drug Design: Predicting Reaction Yields of Suzuki Cross-Couplings by Leveraging AbbVie’s 15-Year Parallel Library Data Set43Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity25AI for organic and polymer synthesis36Designing unique and high-performance Al alloys via machine learning: Mitigating data bias through active learning24Harnessing Electro-Descriptors for Mechanistic and Machine Learning Analysis of Photocatalytic Organic Reactions21Reaction rebalancing: a novel approach to curating reaction databases6Advancements and Challenges of Artificial Intelligence in Drug Discovery and Synthetic Organic Chemistry0Self-Driving Laboratories for Chemistry and Materials Science674Data-Driven Insights into the Transition-Metal-Catalyzed Asymmetric Hydrogenation of Olefins10Intermediate Knowledge Enhanced the Performance of N-Acylation Yield Prediction Model0Parallel and High Throughput Reaction Monitoring with Computer Vision14Parallel and High Throughput Reaction Monitoring with Computer Vision4Ru-Catalyzed Asymmetric Hydrogenation of α,β-Unsaturated γ-Lactams34Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis17Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science19Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis7Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case1Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case14Machine learning-guided strategies for reaction conditions design and optimization49High-throughput experimentation and machine learning-promoted synthesis of α-phosphoryloxy ketones via Ru-catalyzed P(O)O-H insertion reactions of sulfoxonium ylides14Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔ G ≠ versus Enantiomeric Excess18Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔG≠ versus Enantiomeric Excess3The future of machine learning for small-molecule drug discovery will be driven by data41Machine Learning Accelerated Discovery of Entropy-Stabilized Oxide Catalysts for Catalytic Oxidation58Switch on amine substrate reactivity towards hexaazaisowurtzitane cage: Insights from a tailored machine learning model105Challenges with Literature-Derived Data in Machine Learning for Yield Prediction: A Case Study on Pd-Catalyzed Carbonylation Reactions6Estimation of multicomponent reactions’ yields from networks of mechanistic steps12Machine Learning Application for Functional Properties Prediction in Magnetic Materials7A Database of Experiments for the Development of Sulfide-Type Solid Electrolytes1Calibration-free quantification and automated data analysis for high-throughput reaction screening5On the Importance of Data – Introducing Perspectives as New Manuscript Type to SYNLETT0AI molecular catalysis: where are we now?30Intermediate knowledge enhanced the performance of the amide coupling yield prediction model14Leveraging NDE Data for Reusability and Insights0Leveraging NDE Data for Reusability and Insights0Optimising Materials Properties with Minimal Data: Lessons from Vanadium Catalyst Modelling1Predicting reaction conditions: a data-driven perspective10Reaction development: a student's checklist15Simple User‐Friendly Reaction Format16Unveiling the knowledge of a RAFT polymerization database obtained from an automated parallel synthesizer1Machine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor3Database Construction for the Virtual Screening of the Ruthenium-Catalyzed Hydrogenation of Ketones5Machine learning-guided space-filling designs for high throughput liquid formulation development8High-throughput experimentation and machine learning accelerated synthesis of organophosphorus compounds1Revealing the Relationship between Publication Bias and Chemical Reactivity with Contrastive Learning12Enhanced Regioselectivity Prediction of sp2 C–H Halogenation via Negative Data Augmentation and Multimodel Integration8Evaluating Predictive Accuracy in Asymmetric Catalysis: A Machine Learning Perspective on Local Reaction Space8Electrolytomics: A Unified Big Data Approach for Electrolyte Design and Discovery43Carbohydrate Synthesis is Entering the Data‐Driven Digital Era3Stabilities and Limitations in the Reactivity of Phosphorus Ylide‐Based Aluminum– and Gallium–Carbon Ambiphiles: A Combined Experimental and Computational Approach1Local reaction condition optimization via machine learning1Research progress of machine learning in the field of photocatalysis applications21Chiral Intelligence: The Artificial Intelligence‐Driven Future of Chiroptical Properties8Exploring BERT for Reaction Yield Prediction: Evaluating the Impact of Tokenization, Molecular Representation, and Pretraining Data Augmentation9Machine learning-assisted controllable preparation of multicolor phosphorescent carbon dots9Enantioconvergent Synthesis of α‐Fluoroalkyl Alcohols Enabled by Photocatalytic Radical Brook Rearrangement2Enantioconvergent Synthesis of α‐Fluoroalkyl Alcohols Enabled by Photocatalytic Radical Brook Rearrangement28Directed Cascade C–H Functionalization/2-Fold Annulation with Vinylcyclopropanes: Access to Tetrahydrobenzo[g]isochromen-10-ones5

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