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RESEARCH PAPER · Nature Reviews Chemistry · 2019

Synthetic organic chemistry driven by artificial intelligence

A. Filipa Almeida, Rui Moreira, Tiago Rodrigues

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Synthetic organic chemistry driven by artificial intelligenceOriginalMachine learning for target discovery in drug development61The good, the bad, and the ugly in chemical and biological data for machine learning53Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence65Machine Learning Transition Temperatures from 2D Structure10A Computer Conquers Tactical Combinations3AI on a chip140Correction: Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence13Digitising chemical synthesis in automated and robotic flow49Artificial Intelligence to Power the Future of Materials Science and Engineering154More Twins in the Scientific Literature of the 21st Century0More Twins in the Scientific Literature of the 21st Century2Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning130Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning24Learning Molecular Representations for Medicinal Chemistry222Transfer Learning: Making Retrosynthetic Predictions Based on a Small Chemical Reaction Dataset Scale to a New Level35Artificial intelligence in chemistry and drug design165Machine Learning for Chemical Synthesis7Electronic Structure Modeling of Metal–Organic Frameworks329Effective Molecular Descriptors for Chemical Accuracy at DFT Cost: Fragmentation, Error-Cancellation, and Machine Learning40Elevating Chemistry Research with a Modern Electronics Toolkit82Machine learning approaches for the prediction of materials properties255A Mild Method for Making MIDA Boronates22Organic Superbases in Recent Synthetic Methodology Research116Mapping the resources and approaches facilitating computer-aided synthesis planning37Automated Polymer Purification Using Dialysis24Automated synthesis: current platforms and further needs39Powering the Future: How Can Electrochemistry Make a Difference in Organic Synthesis?524Crowdsourcing and open innovation in drug discovery: recent contributions and future directions20Solving Chemistry Problems via an End-to-End Approach: A Proof of Concept10Molecular structure incorporated deep learning approach for the accurate interfacial tension predictions4Time Economy in Total Synthesis136The Role of Machine Learning in the Understanding and Design of Materials433Machine Learning Methods in Drug Discovery456A Recommender System for Inverse Design of Polycarbonates and Polyesters28Multi-Class Metabolic Pathway Prediction by Graph Attention-Based Deep Learning Method16Fragment-based drug discovery: opportunities for organic synthesis72Predicting glycosylation stereoselectivity using machine learning62Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis129Atomic-level insight into reasonable design of metal-based catalysts for hydrogen oxidation in alkaline electrolytes143ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision25DeepReac+: deep active learning for quantitative modeling of organic chemical reactions52In-flow enantioselective homogeneous organic synthesis3Toward autonomous design and synthesis of novel inorganic materials159Transformative Applications of Machine Learning for Chemical Reactions4Machine Learning in FET-based Chemical and Biological Sensors: A Mini Review5Chemistry 2030: A Roadmap for a New Decade56Chemistry 2030: A Roadmap for a New Decade25Machine learning made easy for optimizing chemical reactions21Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix11Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions72Organic Electrochemistry: Molecular Syntheses with Potential860Taking the leap between analytical chemistry and artificial intelligence: A tutorial review175Organic reactivity from mechanism to machine learning175Progress in the Theory of X-ray Spectroscopy: From Quantum Chemistry to Machine Learning and Ultrafast Dynamics78Automation and computer-assisted planning for chemical synthesis189Artificial intelligence in drug discovery: recent advances and future perspectives458Predicting Reaction Yields via Supervised Learning187Extraction of organic chemistry grammar from unsupervised learning of chemical reactions296Development of Xanthene‐Based Fluorescent Dyes: Machine Learning‐Assisted Prediction vs. TD‐DFT Prediction and Experimental Validation18Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem8Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments66Machine Learning for Chemical Reactions437Computational Analysis of Synthetic Planning: Past and Future19Future directions of chemical theory and computation6Ethical Artificial Intelligence in Chemical Research and Development: A Dual Advantage for Sustainability23Acceleration of Baylis-Hillman Reaction using Ionic Liquid Supported Organocatalyst2Machine Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis3Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention177Improved iGAL 2.0 Metric Empowers Pharmaceutical Scientists to Make Meaningful Contributions to United Nations Sustainable Development Goal 1289Design and Application of a Screening Set for Monophosphine Lig-ands in Metal Catalysis7Combating small-molecule aggregation with machine learning21Deep learning in retrosynthesis planning: datasets, models and tools99How Biological Concepts and Evolutionary Theories Are Inspiring Advances in Machine Intelligence3Computer-aided retrosynthetic design: fundamentals, tools, and outlook37The future directions of synthetic chemistry3Machine-Learning-Guided Discovery of 19 F MRI Agents Enabled by Automated Copolymer Synthesis155The Evolution of Data-Driven Modeling in Organic Chemistry185Role of artificial intelligence in chemistry26Artificial intelligence in research and development for sustainability: the centrality of explicability and research data management23Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development94Optical deciphering of multinary chiral compound mixtures through organic reaction based chemometric chirality sensing36Opportunities and challenges of organic flow battery for electrochemical energy storage technology81Storing energy with molecular photoisomers253Towards Property Profiling: SYNTHESIS and SAR Probing of New Tetracyclic Diazaphenothiazine Analogues12Computer-aided design of molecular machines: techniques, paradigms and difficulties8Mechanism of Antiradical Activity of Newly Synthesized 4,7-Dihydroxycoumarin Derivatives-Experimental and Kinetic DFT Study19Chemical Reactivity Prediction: Current Methods and Different Application Areas17A review of automated and data-driven approaches for pathway determination and reaction monitoring in complex chemical systems19Deriving intuition in catalyst design with machine learning5Machine learning activation energies of chemical reactions83Multiscale strategies for describing environment effects: From solvents to biomatrices2Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources191MgO/CaO Nanocomposite Facilitates Economical Production of d -Fructose and d -Allulose Using Glucose and Its Response Prediction Using a DNN Model23Realizing Quantum Technologies in Nanomaterials and Nanoscience25Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?21Small molecules and their impact in drug discovery: A perspective on the occasion of the 125th anniversary of the Bayer Chemical Research Laboratory198AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge60Artificial Intelligence in Interdisciplinary Life Science and Drug Discovery Research20Artificial intelligence to bring nanomedicine to life189High-throughput exploration of halide perovskite compositionally-graded films and degradation mechanisms36Unified Deep Learning Model for Multitask Reaction Predictions with Explanation129Optimal control of quantum thermal machines using machine learning42How to embrace the age of intelligence——taking chemistry as an example1Artificial Intelligence and Machine Learning in Medicinal Chemistry and Validation of Emerging Drug Targets20New era of artificial chemist1Semiconducting Paddle-Wheel Metal–Organic Complex with a Compact Cu–S Cage5Software tools for green and sustainable chemistry25Predicting tensorial molecular properties with equivariant machine learning models47Construction of Legal Reporting Information Platform Based on Natural Optimization Algorithm0Machine 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 sciences180Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling57Mechanistic Inference from Statistical Models at Different Data-Size Regimes37Against theory-motivated experimentation: Can random experimental choice lead to better theories?14α-Metalated Isocyanides Toward a Tangible Reagent Space5AI-Assisted Fusion of Scanning Electrochemical Microscopy Images Using Novel Soft Probe21Ionic Liquids Curated by Machine Learning for Metal Extraction25Microfluidic asymmetrical synthesis and chiral analysis10The green chemistry of chalcones: Valuable sources of privileged core structures for drug discovery42A generalized-template-based graph neural network for accurate organic reactivity prediction57Roadmap to Pharmaceutically Relevant Reactivity Models Leveraging High-Throughput Experimentation17Self-Supervised Molecular Pretraining Strategy for Low-Resource Reaction Prediction Scenarios10Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities50Patterning Wettability for Open-Surface Fluidic Manipulation: Fundamentals and Applications133Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis51Design of Biocompatible Monolayers and Polymeric Materials using Informatics0Minimal data requirements for accurate compound activity prediction using machine learning methods of different complexity27A Comprehensive Review on Machine Learning and Deep Learning Methods in Drug Discovery2Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery138Exposing the Limitations of Molecular Machine Learning with Activity Cliffs328Reactions in single-molecule junctions91Combined ultrasound-microwave assisted synthesis of aripiprazole: Process optimization using RSM-ANN8Specific contributions of artificial intelligence to interdisciplinary life science research – exploring and communicating new opportunities2Engineering discrete synthetic macromolecules for biomedical applications16Digital advancements in smart materials design and multifunctional coating manufacturing46A focus on the use of real-world datasets for yield prediction3CatBoost-Based Framework for Intelligent Prediction and Reaction Condition Analysis of Coupling Reaction2Optimized synthesis of anti-COVID-19 drugs aided by retrosynthesis software2Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis1Transcription between human-readable synthetic descriptions and machine-executable instructions: an application of the latest pre-training technology3Uncertainty quantification of spectral predictions using deep neural networks13Machine‐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 Phenols1EFMC: Trends in Medicinal Chemistry and Chemical Biology15Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights24Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges88Generative Kinase Inhibitor Modeling Viewed From a Medicinal Chemistry Perspective5Recent Advances in the Chemical Synthesis of β-Nicotinamide Mononucleotide8An efficiency-driven, correlation-based feature elimination strategy for small datasets25

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