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RESEARCH PAPER · Journal of the American Chemical Society · 2022

Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings

Jules Schleinitz, Maxime Langevin, Yanis Smail, Benjamin Wehnert, Laurence Grimaud, Rodolphe Vuilleumier

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Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O CouplingsOriginalRoadmap to Pharmaceutically Relevant Reactivity Models Leveraging High-Throughput Experimentation17Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery138Building robust machine learning models for small chemical science data: the case of shear viscosity of fluids7Machine-Learning-Assisted Design of Highly Tough Thermosetting Polymers76ACCELERATING DRUG DISCOVERY BY HIGH-THROUGHPUT EXPERIMENTATION1Highlights from the 56th Bürgenstock Conference on Stereochemistry 20230Predicting ruthenium catalysed hydrogenation of esters using machine learning10The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions39MetaRF: attention-based random forest for reaction yield prediction with a few trails30High-throughput screening of multimetallic catalysts for three-way catalysis1Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit39Rapid planning and analysis of high-throughput experiment arrays for reaction discovery83Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity2Extraction Yield Prediction for the Large-Scale Recovery of Botanicals1High‐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 Ylides9High-throughput synthesis provides data for predicting molecular properties and reaction success28Machine learning approach for predicting the yield of pyrroles and dipyrromethanes condensation reactions with aldehydes13High-throughput screening of multimetallic catalysts for three-way catalysis3Organopalladium Catalysis as a Proving Ground for Data-Rich Approaches to Reaction Development and Quantitative Predictions8Extraction yield prediction for the large-scale recovery of cannabinoids6Dataset Design for Building Models of Chemical Reactivity94When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges64Cost-informed Bayesian reaction optimization17Regio-MPNN: predicting regioselectivity for general metal-catalyzed cross-coupling reactions using a chemical knowledge informed message passing neural network8Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective26Machine Learning-Based Yield Prediction for First-Row Transition Metal Catalyzed Cross-Coupling Reactions0Bridging the information gap in organic chemical reactions28Incorporating 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 Enantioselectivity25Computer‐Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions15Data-Driven Insights into the Transition-Metal-Catalyzed Asymmetric Hydrogenation of Olefins10Machine 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 Excess3Challenges with Literature-Derived Data in Machine Learning for Yield Prediction: A Case Study on Pd-Catalyzed Carbonylation Reactions6Machine learning-based yield prediction for transition metal-catalyzed cross-coupling reactions4AI molecular catalysis: where are we now?30Investigation of arene and heteroarene nitration supported by high-throughput experimentation and machine learning0Molecular Machine Learning Approach to Enantioselective C–H Bond Activation Reactions: From Generative AI to Experimental Validation5Predicting reaction conditions: a data-driven perspective10Recommending reaction conditions with label ranking6Database Construction for the Virtual Screening of the Ruthenium-Catalyzed Hydrogenation of Ketones5CLAIRE: a contrastive learning-based predictor for EC number of chemical reactions16Using Classifiers To Predict Catalyst Design for Polyketone Microstructure18Predicting and Explaining Yields with Machine Learning for Carboxylated Azoles and Beyond6C–H Aminoalkylation of 5-Membered Heterocycles: Influence of Descriptors, Data Set Size, and Data Quality on the Predictiveness of Machine Learning Models and Expansion of the Substrate Space Beyond 1,3-Azoles1Multi‐modal Homogeneous Chemical Reaction Performance Prediction with Graph and Chemical Language Information7Designing Target-specific Data Sets for Regioselectivity Predictions on Complex Substrates18YieldFCP: Enhancing Reaction Yield Prediction via Fine-grained Cross-modal Pre-training6Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data19Evaluating Predictive Accuracy in Asymmetric Catalysis: A Machine Learning Perspective on Local Reaction Space8A meta-learning approach for selectivity prediction in asymmetric catalysis11Local reaction condition optimization via machine learning1Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning18Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides18Machine learning predictions for regioselectivity of hydroformylation reactions: leveraging limited data for high-precision results0Developing Pharmaceutically Relevant Pd-Catalyzed C–N Coupling Reactivity Models Leveraging High-Throughput Experimentation23Study on Phosphorus Compound/Catechol‐Catalyzed Dehydrative Amidation and Its Database Development for Machine Learning2Predicting Reactivity with Machine Learning1Optimizing Vanadium-Catalyzed Epoxidation Reactions: Machine-Learning-Driven Yield Predictions and Data Augmentation6Organometallic-type reactivity of stable organoboronates for selective (hetero)arene C−H/C-halogen borylation and beyond5A unified pre-trained deep learning framework for cross-task reaction performance prediction and synthesis planning14Optimizing Model Learning Performance on a Challenging Heck Reaction Yield Data Set2Automating structure–activity analysis for electrochemical nitrogen reduction catalyst design through multi-agent collaborations19Data‐Driven Modeling of N,N′ ‐Dioxide/Metal‐Catalyzed Asymmetric Michael Additions11Data‐Driven Modeling of N,N′ ‐Dioxide/Metal‐Catalyzed Asymmetric Michael Additions1Efficient Machine Learning for Yield Prediction in Transition Metal-Catalyzed meta -C(sp 2 )–H Bond Activation and Other Reactions2An overview of reaction outcome prediction with physics-based and data-driven methods2Cheminformatics in life cycle assessment: Advancing solvent, toxicology, and chemical synthesis for sustainable innovation0Designing less noble yet high-performing multimetallic catalysts for catalytic converters using a high-throughput approach0Protocol-guided evaluation of reaction yield prediction under predictable distribution shifts: a review0Synthetic Applicability Domain (SynAD): Navigating Chemical Space for Reliable AI‐Driven Reaction Prediction3Synthetic Applicability Domain (SynAD): Navigating Chemical Space for Reliable AI‐Driven Reaction Prediction0Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning3Precision orientation of hydrogen evolution performance of iron-based electrocatalysts empowered by machine learning: From electronic structure regulation to rational construction0One pot sulfur(IV) fluoride-mediated deoxyaminations for the syntheses of 3,3-arylamino oxetanes and azetidines0Data-Driven, Mechanistically Guided Prediction of Yield and Chemoselectivity in SuFEx Reactions4Development of Deep-Learning Models that Predict Quantitative Protein-Ligand Interactions in Glycobiology as a part of a Capstone Course0AI for science: Progress, challenges, and perspectives2Machine Learning for Synthetic Organic Chemistry: Methods, Applications, and Best Practices0Predicting Reactivity and Enantioselectivity from Small Experimental Datasets: A Case Study in Asymmetric Magnesium Catalysis0Integrating Mechanistic Knowledge into Machine Learning Enables Improved Reaction Prediction in Low‐Data Regimes0Enabling Data-Efficient Machine Learning through Multi-Target Training Set Design via Greedy Neighborhood Coverage0Machine Learning Prediction for Transition-Metal-Catalyzed Direct C-3 Arylation of Imidazo[1,2-a]pyridines0

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