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RESEARCH PAPER · Accounts of Chemical Research · 2018

Machine Learning in Computer-Aided Synthesis Planning

Connor W. Coley, William H. Green, Klavs F. Jensen

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Machine Learning in Computer-Aided Synthesis PlanningOriginalVirtual Chemical Libraries205An Autonomous Self-Optimizing Flow Reactor for the Synthesis of Natural Product Carpanone128Across‐the‐World Automated Optimization and Continuous‐Flow Synthesis of Pharmaceutical Agents Operating Through a Cloud‐Based Server89Using Machine Learning To Predict Suitable Conditions for Organic Reactions422A graph-convolutional neural network model for the prediction of chemical reactivity674The promise of artificial intelligence in chemical engineering: Is it here, finally?664High Throughput Strategies for the Discovery and Optimization of Catalytic Reactions165A Transformer Model for Retrosynthesis122Designing promising molecules for organic solar cells via machine learning assisted virtual screening115Selection of cost-effective yet chemically diverse pathways from the networks of computer-generated retrosynthetic plans75Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines112Virtual Compound Libraries in Computer-Assisted Drug Discovery70Enhancing Retrosynthetic Reaction Prediction with Deep Learning Using Multiscale Reaction Classification106Computational Ligand Descriptors for Catalyst Design406Mind and machine in drug design70Advances of machine learning in molecular modeling and simulation128Next-Generation Experimentation with Self-Driving Laboratories421Retrosynthetic design of metabolic pathways to chemicals not found in nature120Have marine natural product drug discovery efforts been productive and how can we improve their efficiency?79Overview of Recent Strategic Advances in Medicinal Chemistry181CGRtools: Python Library for Molecule, Reaction, and Condensed Graph of Reaction Processing78Learning Retrosynthetic Planning through Simulated Experience179The digitization of organic synthesis122Deep Learning in Chemistry561RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application224Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data90Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery1022Advancing Drug Discovery via Artificial Intelligence649Deep learning in drug discovery: opportunities, challenges and future prospects350A robotic platform for flow synthesis of organic compounds informed by AI planning1052Synthetic organic chemistry driven by artificial intelligence303Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction928Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis219BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules322Autonomous Discovery in the Chemical Sciences Part I: Progress317Linking synthesis and structure descriptors from a large collection of synthetic records of zeolite materials123Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain163An intelligent decision support system for production planning based on machine learning123Synergy Between Expert and Machine‐Learning Approaches Allows for Improved Retrosynthetic Planning75Chemical product design – recent advances and perspectives90Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns181Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks80Rethinking drug design in the artificial intelligence era880Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks249Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules124Automatic retrosynthetic route planning using template-free models216Generating transition states of isomerization reactions with deep learning79Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex178Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy403QSAR without borders878Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization81Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning68The Exploration of Chemical Reaction Networks202A Structure-Based Platform for Predicting Chemical Reactivity288The power of deep learning to ligand-based novel drug discovery76Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis243Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry154Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems117Artificial intelligence in chemistry and drug design165Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions120Continuous Flow Upgrading of Selected C2–C6 Platform Chemicals Derived from Biomass398Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions68Organic Superbases in Recent Synthetic Methodology Research116Digital Reticular Chemistry171Molecular Machine Learning: The Future of Synthetic Chemistry?72Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration135Machine-Learning-Driven Synthesis of Carbon Dots with Enhanced Quantum Yields274Drug discovery with explainable artificial intelligence1049Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks97Targeted sequence design within the coarse-grained polymer genome141Emerging materials intelligence ecosystems propelled by machine learning441tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes138The Role of Machine Learning in the Understanding and Design of Materials433AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning378Automation and data-driven design of polymer therapeutics104Uncertainty quantification in drug design95BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules102Automated engineering of synthetic metabolic pathways for efficient biomanufacturing77Applications of Deep Learning in Molecule Generation and Molecular Property Prediction382Accelerating organic solar cell material's discovery: high-throughput screening and big data129Chemical data intelligence for sustainable chemistry68Predicting enzymatic reactions with a molecular transformer84Toward autonomous design and synthesis of novel inorganic materials159Toward Machine Learning-Enhanced High-Throughput Experimentation155RetroBioCat as a computer-aided synthesis planning tool for biocatalytic reactions and cascades253Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions91Building a Parallel Corpus for English Translation Teaching Based on Computer-aided Translation Software70Process systems engineering – The generation next?260Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery74Graph neural networks for automated de novo drug design192Automated Quantification of Hydroxyl Reactivities: Prediction of Glycosylation Reactions87Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions72Taking the leap between analytical chemistry and artificial intelligence: A tutorial review175Organic reactivity from mechanism to machine learning175Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias112Automation and computer-assisted planning for chemical synthesis189Predicting Reaction Yields via Supervised Learning187Critical assessment of AI in drug discovery106Electrosynthetic Screening and Modern Optimization Strategies for Electrosynthesis of Highly Value‐added Products77RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions129Automated Chemical Reaction Extraction from Scientific Literature104Biocatalysis685Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems840Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits172Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks93Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning78Improved iGAL 2.0 Metric Empowers Pharmaceutical Scientists to Make Meaningful Contributions to United Nations Sustainable Development Goal 1289Atom‐to‐atom Mapping: A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies76The Open Reaction Database324Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network76Enhancing preclinical drug discovery with artificial intelligence180Molecular representations for machine learning applications in chemistry93Machine learning for design principles for single atom catalysts towards electrochemical reactions116Quantum machine learning for chemistry and physics151Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks72Machine learning modeling of family wide enzyme-substrate specificity screens120Inverse Design of Materials by Machine Learning137Unified Deep Learning Model for Multitask Reaction Predictions with Explanation129Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP169Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Machine learning directed multi-objective optimization of mixed variable chemical systems85Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings87A Multi-Objective Active Learning Platform and Web App for Reaction Optimization201Application of Computational Biology and Artificial Intelligence in Drug Design150Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Exposing the Limitations of Molecular Machine Learning with Activity Cliffs328Merging enzymatic and synthetic chemistry with computational synthesis planning81A field guide to flow chemistry for synthetic organic chemists393Graph Neural Networks for Molecules84On the use of real-world datasets for reaction yield prediction141Chemical reaction networks and opportunities for machine learning141Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development343Machine learning for nanoplasmonics149The rise of self-driving labs in chemical and materials sciences570Machine learning-enabled retrobiosynthesis of molecules120A Brief Introduction to Chemical Reaction Optimization437Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers70Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing81Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network78Machine Learning for Chemistry: Basics and Applications102Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning89Scope of machine learning in materials research—A review230Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR397Chemprop: A Machine Learning Package for Chemical Property Prediction663Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications115Autonomous chemistry: Navigating self-driving labs in chemical and material sciences95Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development102A review of machine learning applications in polymer composites: advancements, challenges, and future prospects135AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs203Graph Neural Networks in Modern AI-Aided Drug Discovery87

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