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RESEARCH PAPER · Proceedings of the National Academy of Sciences · 2020

A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation

Sukriti Singh, Monika Pareek, Avtar Changotra, Sayan Banerjee, Bangaru Bhaskararao, P. Balamurugan, Raghavan B. Sunoj

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A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenationOriginalMachine learning in experimental materials chemistry66Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors169Computational insights into metal-catalyzed asymmetric hydrogenation11Deep Residual Learning With Dilated Causal Convolution Extreme Learning Machine7Machine learning prediction of hydrogen atom transfer reactivity in photoredox-mediated C–H functionalization33Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts105Toward Machine Learning-Enhanced High-Throughput Experimentation155Machine learning for advanced energy materials208Quantitative Prediction on the Enantioselectivity of Multiple Chiral Iodoarene Scaffolds Based on Whole Geometry0Kernel Methods for Predicting Yields of Chemical Reactions2Dreams, False Starts, Dead Ends, and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts56Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions42Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions15Reducing Challenges in Organic Synthesis with Stereoselective Hydrogenation and Tandem Catalysis64Data-Driven Catalyst Optimization for Stereodivergent Asymmetric Synthesis of α-Allyl Carboxylic Acids by Iridium/boron Hybrid Catalysis0A review on extreme learning machine557Evaluation of a Data-Driven, Machine Learning Approach for Identifying Potential Candidates for Environmental Catalysts: From Database Development to Prediction17Data-driven catalyst optimization for stereodivergent asymmetric synthesis of α-allyl carboxylic acids by iridium/boron hybrid catalysis0Advancement in Organic Synthesis Through High Throughput Experimentation41Prediction 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 Learning6Kernel Methods for Predicting Yields of Chemical Reactions47Optical deciphering of multinary chiral compound mixtures through organic reaction based chemometric chirality sensing36Coming of Age of Computational Chemistry from a Resilient Past to a Promising Future2The roles of computer-aided drug synthesis in drug development29Data-driven catalyst optimization for stereodivergent asymmetric synthesis by iridium/boron hybrid catalysis53A machine learning approach for predicting the nucleophilicity of organic molecules28Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 111Artificial intelligence: machine learning for chemical sciences104A transfer learning protocol for chemical catalysis using a recurrent neural network adapted from natural language processing37Deep learning for enantioselectivity predictions in catalytic asymmetric β-C–H bond activation reactions31Machine learnt patterns in rhodium-catalysed asymmetric Michael addition using chiral diene ligands10Machine learning studies on asymmetric relay Heck reaction—Potential avenues for reaction development20An Ensemble Structure and Physicochemical (SPOC) Descriptor for Machine‐Learning Prediction of Chemical Reaction and Molecular Properties35Evaluation guidelines for machine learning tools in the chemical sciences180Machine Learning Prediction of Structure‐Performance Relationship in Organic Synthesis16A transfer learning approach for reaction discovery in small data situations using generative model15Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors1Identification of ORR activity of random graphene-based systems using the general descriptor and predictive model equation38Machine learning integrated photocatalysis: progress and challenges94Predicting ruthenium catalysed hydrogenation of esters using machine learning10Prediction of the enantiomeric excess value for asymmetric transfer hydrogenation based on machine learning8Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis1Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols39Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols1Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**64Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**2Enantioselectivity prediction of pallada-electrocatalysed C–H activation using transition state knowledge in machine learning80Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges88Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis—Challenges and Opportunities7High-Throughput Computational Screening of Bioinspired Dual-Atom Alloys for CO2 Activation54Genetic Algorithms for the Discovery of Homogeneous Catalysts14Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge73ML meets MLn: Machine learning in ligand promoted homogeneous catalysis20Feed-Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction24MIM-ML: A Novel Quantum Chemical Fragment-Based Random Forest Model for Accurate Prediction of NMR Chemical Shifts of Nucleic Acids15Recent Applications of Machine Learning in Molecular Property and Chemical Reaction Outcome Predictions51Machine Learning Study on Microwave-Assisted Batch Preparation and Oxygen Reduction Performance of Fe–N–C Catalysts13Machine learning approach for predicting the yield of pyrroles and dipyrromethanes condensation reactions with aldehydes13Intelligent design and synthesis of energy catalytic materials25Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions23CatScore: evaluating asymmetric catalyst design at high efficiency2Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts26Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry93Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures74Paving the road towards automated homogeneous catalyst design32Advances in machine learning with chemical language models in molecular property and reaction outcome predictions29Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction18From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts69Impact of Model Selection and Conformational Effects on the Descriptors for In Silico Screening Campaigns: A Case Study of Rh-Catalyzed Acrylate Hydrogenation9Leveraging Limited Experimental Data with Machine Learning: Differentiating a Methyl from an Ethyl Group in the Corey–Bakshi–Shibata Reduction14Discovery of wurtzite solid solutions with enhanced piezoelectric response using machine learning3Universal descriptors of quasi transition states for small-data-driven asymmetric catalysis prediction in machine learning model12Deep Kernel learning for reaction outcome prediction and optimization19AI for organic and polymer synthesis36When 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 Optimization24ROBERT : Bridging the Gap Between Machine Learning and Chemistry49Reinforcement Learning for Improving Chemical Reaction Performance15Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔ G ≠ versus Enantiomeric Excess18Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔG≠ versus Enantiomeric Excess3Machine Learned Potential Enables Molecular Dynamics Simulation to Predict the Experimental Branching Ratios in the NO Release Channel of Nitroaromatic Compounds2AI-Driven Discovery of Asymmetric Pauson–Khand Reactions: A New Toolbox in a Synthetic Chemist’s Treasure1Reticulating Crystalline Porous Materials for Asymmetric Heterogeneous Catalysis43A machine learning-driven prediction of Hammett constants using quantum chemical and structural descriptors5Connecting the complexity of stereoselective synthesis to the evolution of predictive tools8Molecular Machine Learning Approach to Enantioselective C–H Bond Activation Reactions: From Generative AI to Experimental Validation5Database Construction for the Virtual Screening of the Ruthenium-Catalyzed Hydrogenation of Ketones5Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach4Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis9Controlling Stereoselectivity with Noncovalent Interactions in Chiral Phosphoric Acid Organocatalysis87Evaluating Predictive Accuracy in Asymmetric Catalysis: A Machine Learning Perspective on Local Reaction Space8Integrating a multitask graph neural network with DFT calculations for site-selectivity prediction of arenes and mechanistic knowledge generation19Machine learning for predicting enantioselectivity in chiral phosphoric acid-catalyzed naphthyl-indole synthesis2The Importance of Atomic Charges for Predicting Site-Selective Ir-, Ru-, and Rh-Catalyzed C–H Borylations6AI Approaches to Homogeneous Catalysis with Transition Metal Complexes47Study on Phosphorus Compound/Catechol‐Catalyzed Dehydrative Amidation and Its Database Development for Machine Learning2Machine learning-assisted prediction and optimization of electrocatalytic activity16Predicting Reactivity with Machine Learning1The Logic of Proof of Concept Research0Empowering Reactivity Predictions through Noise-Based Data Augmentation15Transfer learning from custom-tailored virtual molecular databases to real-world organic photosensitizers for catalytic activity prediction1Reaction-conditioned generative model for catalyst design and optimization with CatDRX9Generality‐Driven Optimization of Enantio‐ and Regioselective Mono‐Reduction of 1,2‐Dicarbonyls by High‐Throughput Experimentation and Machine Learning5Generality‐Driven Optimization of Enantio‐ and Regioselective Mono‐Reduction of 1,2‐Dicarbonyls by High‐Throughput Experimentation and Machine Learning0Data‐Driven Modeling of N,N′ ‐Dioxide/Metal‐Catalyzed Asymmetric Michael Additions11Data‐Driven Modeling of N,N′ ‐Dioxide/Metal‐Catalyzed Asymmetric Michael Additions1Explainable artificial intelligence for materials discovery: application to catalysts for the HER and ORR13Effect of Molecular Descriptors on the Development of Machine Learning Models for the Prediction of Yield Sooting Index2Efficient Machine Learning for Yield Prediction in Transition Metal-Catalyzed meta -C(sp 2 )–H Bond Activation and Other Reactions2Data-driven approach to elucidate the correlation between photocatalytic activity and rate constants from excited states6Chemistry-informed deep learning model for predicting stereoselectivity and absolute configuration in asymmetric hydrogenation10An overview of reaction outcome prediction with physics-based and data-driven methods2CHIMERA: A Multi-Level Representation Framework for Predicting Asymmetric Catalytic Reactions0Machine learning models for catalytic asymmetric reactions of simple alkenes: from enantioselectivity predictions to chemical insights4Machine Learning‐Driven Innovations in Energy and Environmental Catalysis: A Minireview0Artificial intelligence-enabled chiral functional materials design4Structure–Selectivity Relationship Study of IDPi Using Fragment and Remote Site Descriptors0Structure–Selectivity Relationship Study of IDPi Using Fragment and Remote Site Descriptors0Capturing stereochemical information with AI: Driving stereoselectivity prediction and rational design in asymmetric catalysis0Benchmarking molecular representations and machine learning algorithms for asymmetric catalysis: a palladium-catalysed decarboxylative asymmetric allylic alkylation case study0ChemDraw-Guided Design of Descriptors for Quantitative Statistical Models0Evaluation of Machine Learning Models for Condition Optimization in Diverse Amide Coupling Reactions0Predicting Enantioselectivity of Ruthenium-Catalyzed Ketone Hydrogenation with 3D Structure-Based Deep Learning0Automated Reaction Network Enumeration and Profiling Platform for Mechanism-Guided Catalyst Design and Reactivity Optimization03D, Structure-Based, Deep Learning Approach to Predict the Regioselectivity of Olefin Hydroformylations0Machine Learning for Synthetic Organic Chemistry: Methods, Applications, and Best Practices0Predicting Reactivity and Enantioselectivity from Small Experimental Datasets: A Case Study in Asymmetric Magnesium Catalysis0

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