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RESEARCH PAPER · Chemical Society Reviews · 2020

Machine learning the ropes: principles, applications and directions in synthetic chemistry

Felix Strieth‐Kalthoff, Frederik Sandfort, Marwin Segler, Frank Glorius

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Machine learning the ropes: principles, applications and directions in synthetic chemistryOriginalMolecular Machine Learning: The Future of Synthetic Chemistry?72Electrochemical Oxidative Carbon‐Atom Difunctionalization: Towards Multisubstituted Imino Sulfide Ethers7Electrochemical Oxidative Carbon‐Atom Difunctionalization: Towards Multisubstituted Imino Sulfide Ethers33Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently55Digitization of Organic Synthesis — How Synthetic Organic Chemists Use AI Technology —24Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next25Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning88C–H Activation: Toward Sustainability and Applications833Machine learning made easy for optimizing chemical reactions21Machine Learning Force Fields1584Taking the leap between analytical chemistry and artificial intelligence: A tutorial review175Automation and computer-assisted planning for chemical synthesis189Human-Readable SMILES: Translating Cheminformatics to Chemistry3Human-Readable SMILES: Translating Cheminformatics to Chemistry0Dreams, False Starts, Dead Ends, and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts56Electrosynthetic Screening and Modern Optimization Strategies for Electrosynthesis of Highly Value‐added Products77Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke25A Conformer‐Dependent, Quantitative Quadrant Model14Recent advances in transition metal-catalyzed asymmetric electrocatalysis51C–H activation643Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems840Augmenting Adaptive Machine Learning with Kinetic Modeling for Reaction Optimization20Machine‐Assisted Preparation of a Chiral Diamine Ligand Library and In Silico Screening Using Ab Initio Structural Parameters for Heterogeneous Chiral Catalysts4Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review37Applications of Artificial Intelligence Based Techniques on the Analysis of Chemical Data: a Review4Artificial Intelligence in Chemistry: Current Trends and Future Directions286Machine Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis3Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention177Application of an Electrochemical Microflow Reactor for Cyanosilylation: Machine Learning-Assisted Exploration of Suitable Reaction Conditions for Semi-Large-Scale Synthesis36Predictive Design Model for Low-Dimensional Organic–Inorganic Halide Perovskites Assisted by Machine Learning114Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space69Python for Chemists7SMINBR: An Integrated Network and Chemoinformatics Tool Specialized for Prediction of Two-Component Crystal Formation12Machine learning approach for prediction of the grafting yield in radiation-induced graft polymerization19Deep learning in retrosynthesis planning: datasets, models and tools99Computer‐Aided Living Polymerization Conducted under Continuous‐Flow Conditions†22Is Organic Chemistry Really Growing Exponentially?24Is Organic Chemistry Really Growing Exponentially?6New trends in nonconventional carbon dot synthesis60Machine-Learning-Guided Discovery of 19 F MRI Agents Enabled by Automated Copolymer Synthesis155Structure-Based Relative Energy Prediction Model: A Case Study of Pd(II)-Catalyzed Ethylene Polymerization and the Electronic Effect of Ancillary Ligands6Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives21Quantum computing enhanced machine learning for physico-chemical applications2Artificial Intelligence in Compound Design14Dynamics of Heterogeneous Catalytic Processes at Operando Conditions87A machine learning approach for predicting the nucleophilicity of organic molecules28Digital Microscale Electrochemical Energy Storage Devices for a Fully Connected and Intelligent World46Chemical Reactivity Prediction: Current Methods and Different Application Areas17Machine learning activation energies of chemical reactions83A machine learning approach for predicting the empirical polarity of organic solvents19A transfer learning protocol for chemical catalysis using a recurrent neural network adapted from natural language processing37Data management matters14Deep learning for enantioselectivity predictions in catalytic asymmetric β-C–H bond activation reactions31Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction42Machine learnt patterns in rhodium-catalysed asymmetric Michael addition using chiral diene ligands10Modern machine learning for tackling inverse problems in chemistry: molecular design to realization50Quantum machine learning for chemistry and physics151Sigma profiles in deep learning: towards a universal molecular descriptor67Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources191Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks72MgO/CaO Nanocomposite Facilitates Economical Production of d -Fructose and d -Allulose Using Glucose and Its Response Prediction Using a DNN Model23A Carbon Nanotube Sensor Array for the Label-Free Discrimination of Live and Dead Cells with Machine Learning29Brain neural network, development, microbiome, microbial toxins and COVID-190Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction91AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge60Artificial intelligence to bring nanomedicine to life189Kinetics of the Direct DME Synthesis: State of the Art and Comprehensive Comparison of Semi-Mechanistic, Data-Based and Hybrid Modeling Approaches14Data‐Driven Materials Innovation and Applications187RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software49Machine Learning for Chemical Reactivity: The Importance of Failed Experiments217Maschinelles Lernen zur Vorhersage chemischer Reaktivität: Die Bedeutung “gescheiterter” Experimente8Evaluation guidelines for machine learning tools in the chemical sciences180Ultra-selective tin oxide-based chemiresistive gas sensor employing signal transform and machine learning techniques64On-the-Fly Mass Spectrometry in Digital Microfluidics Enabled by a Microspray Hole: Toward Multidimensional Reaction Monitoring in Automated Synthesis Platforms49Advancing Base Metal Catalysis through Data Science: Insight and Predictive Models for Ni-Catalyzed Borylation through Supervised Machine Learning24Machine Learning Prediction of Structure‐Performance Relationship in Organic Synthesis16Mechanistic Inference from Statistical Models at Different Data-Size Regimes37Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives90VLA-SMILES: Variable-Length-Array SMILES Descriptors in Neural Network-Based QSAR Modeling0Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings87When machine learning meets molecular synthesis56Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects24Auto Machine Learning Assisted Preparation of Carboxylic Acid by TEMPO‐Catalyzed Primary Alcohol Oxidation19Roadmap to Pharmaceutically Relevant Reactivity Models Leveraging High-Throughput Experimentation17Selective functionalization of hindered meta-C–H bond of o-alkylaryl ketones promoted by automation and deep learning31Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case26Machine Learning Assisted Graphdiyne-Based Nanozyme Discovery55Modeling solubility of oxygen in ionic liquids: Chemical structure-based Machine Learning Systems Compared to Equations of State9Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis51Transition state search and geometry relaxation throughout chemical compound space with quantum machine learning18Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search1Selective Detection of Mixtures via a Single Nonselective Sensor—Making the Unworkable Sensor Workable by Machine Learning15Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery138Machine-Learning-Assisted Manipulation and Readout of Molecular Spin Qubits12A Categorical Approach to Synthetic Chemistry2A simple array integrating machine learning for identification of flavonoids in red wines9Bioinformatics approaches: elucidation of novel sites of action, toxicity prediction tool, and perception of bioactive compounds3Inverse design of viral infectivity-enhancing peptide fibrils from continuous protein-vector embeddings16Kinetically-driven reactivity of sulfinylamines enables direct conversion of carboxylic acids to sulfinamides47Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting34Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search27SolvBERT for solvation free energy and solubility prediction: a demonstration of an NLP model for predicting the properties of molecular complexes40Machine Learning C–N Couplings: Obstacles for a General-Purpose Reaction Yield Prediction48Machine‐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**2Machine learning classifies catalytic-reaction mechanisms3Electrochemical Carbon-Ferrier Rearrangement Using a Microflow Reactor and Machine Learning-Assisted Exploration of Suitable Conditions18Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights24Interpretable Machine Learning for Prediction of Post-Fire Self-Healing of Concrete43Machine Learning in Drug Design6Interactive Quantum Chemistry Enabled by Machine Learning, Graphical Processing Units, and Cloud Computing16Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis—Challenges and Opportunities7Machine learning assisted screening of MXenes pseudocapacitive materials36Machine learning-based models for the qualitative classification of potassium ferrocyanide using electrochemical methods19Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures73A Machine Learning Approach to Model Interaction Effects: Development and Application to Alcohol Deoxyfluorination23Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search2Introduction to artificial intelligence and deep learning using interactive electronic programming notebooks5The role of machine learning in carbon neutrality: Catalyst property prediction, design, and synthesis for carbon dioxide reduction62Machine Learning that Proposes Reaction Conditions and Yields for Wittig-type Methylenation of Aldehydes with Bis(iodozincio)methane in a Flow-microreactor5QMugs 1.1: Quantum mechanical properties of organic compounds commonly encountered in reactivity datasets14Computation of CCSD(T)-Quality NMR Chemical Shifts via Δ-Machine Learning from DFT30Predicting the ET(30) parameter of organic solvents via machine learning16Bayesian-optimization-assisted discovery of stereoselective aluminum complexes for ring-opening polymerization of racemic lactide68Artificial Intelligence-Assisted Chemistry1The Power of Automation in Polymer Chemistry: Precision Synthesis of Multiblock Copolymers with Block Sequence Control24Electrostatic Interactions in Asymmetric Organocatalysis46ML meets MLn: Machine learning in ligand promoted homogeneous catalysis20Fake it until you make it? Generative de novo design and virtual screening of synthesizable molecules48Identifying Catalyst Property Descriptors for CO2 Hydrogenation to Methanol via Big-Data Analysis28Process analytical technologies and self-optimization algorithms in automated pharmaceutical continuous manufacturing35A Novel Approach for Identification and Donation of Surplus Food using Machine Learning-based Replate App10Selective identification and quantification of VOCs using metal nanoparticles decorated SnO2 hollow-spheres based sensor array and machine learning51Computer‐Assisted Synthesis Planning2RPBP: Deep Retrosynthesis Reaction Prediction Based on Byproducts6How to train a neural network potential79High‐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 Ylides9Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction207A study on the influence of teacher certification concept on the construction of art majors in universities based on big data analysis0Transforming organic chemistry research paradigms: Moving from manual efforts to the intersection of automation and artificial intelligence5Organopalladium Catalysis as a Proving Ground for Data-Rich Approaches to Reaction Development and Quantitative Predictions8A machine learning approach for predicting the reactivity power of hypervalent iodine compounds7Accelerated chemical science with AI86A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target32Chemoenzymatic multistep retrosynthesis with transformer loops11Combining Hammett σ constants for Δ-machine learning and catalyst discovery4Development of an automated platform for monitoring microfluidic reactors through multi-reactor integration and online (chip-)LC/MS-detection12

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