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RESEARCH PAPER · Chemical Science · 2022

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

Zhengkai Tu, Thijs Stuyver, Connor W. Coley

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Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discoveryOriginalEfficiently predicting and synthesizing intrinsic highly fire-safe polycarbonates with processability10READRetro: Natural Product Biosynthesis Planning with Retrieval-Augmented Dual-View Retrosynthesis1LinChemIn: SynGraph—a data model and a toolkit to analyze and compare synthetic routes12Machine 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 datasets14Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit39Machine Learning-Guided Development of Trialkylphosphine Ni (I) Dimers and Applications in Site-Selective Catalysis43Open-Source Machine Learning in Computational Chemistry59Designing Chemical Reaction Arrays Using Phactor and ChatGPT39In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Learning organo‐transition metal catalyzed reactions by graph neural networks3Prediction of Reaction Performance by Machine Learning Using Streamlined Features: NMR Chemical Shifts as Familiar Descriptors5Full Modification Control over Retrosynthetic Routes for Guided Optimization of Lead Structures4Smart Dope: A Self‐Driving Fluidic Lab for Accelerated Development of Doped Perovskite Quantum Dots47Predictive catalysis: a valuable step towards machine learning47Yoked learning in molecular data science4Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules47Dataset Design for Building Models of Chemical Reactivity94Autonomous nanomanufacturing of lead-free metal halide perovskite nanocrystals using a self-driving fluidic lab29Empowering research in chemistry and materials science through intelligent algorithms15Predictive modeling of the heat of formation of sulfur hexafluoride using data science techniques4When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges64A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target32Data science-centric design, discovery, and evaluation of novel synthetically accessible polyimides with desired dielectric constants13Every atom counts: predicting sites of reaction based on chemistry within two bonds0High-throughput label-free opioid receptor binding assays using an automated desorption electrospray ionization mass spectrometry platform13Improving reproducibility through condition-based sensitivity assessments: application, advancement and prospect46Interpretable machining learning assisted insights into bifunctional squaramide catalyzed ring-opening polymerization of lactide3PythiaCHEM : a user-friendly machine learning toolkit for chemistry2Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview14Biomimetic Frustrated Lewis Pair Catalysts for Hydrogenation of CO to Methanol at Low Temperatures5LinChemIn: Route Arithmetic─Operations on Digital Synthetic Routes4Computer-assisted design of CGC catalysts for ethylene/1-octene copolymerization: A combined DFT and artificial neural network approach7Accelerated end-to-end chemical synthesis development with large language models11RETRACTED ARTICLE: Comparing ANI-2x, ANI-1ccx neural networks, force field, and DFT methods for predicting conformational potential energy of organic molecules19Benchmarking Strategies of Sustainable Process Chemistry Development: Human-Based, Machine Learning, and Quantum Mechanics9Deep Kernel learning for reaction outcome prediction and optimization19OpenChemIE: An Information Extraction Toolkit for Chemistry Literature32Integrating digital chemistry within the broader chemistry community11Bidirectional Chemical Intelligent Net: A unified deep learning–based framework for predicting chemical reactions0Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms31Computer‐Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions15Re-evaluating retrosynthesis algorithms with Syntheseus38Data-Driven Insights into the Transition-Metal-Catalyzed Asymmetric Hydrogenation of Olefins10An analysis of published synthetic routes, route targets and reaction types (2000 – 2020)0Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case1Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case145th International Symposium on Synthesis and Catalysis (ISySyCat2023)1An Analysis of Published Synthetic Routes, Route Targets, and Reaction Types (2000–2020)9Machine Learned Potential Enables Molecular Dynamics Simulation to Predict the Experimental Branching Ratios in the NO Release Channel of Nitroaromatic Compounds2Assessment of fine-tuned large language models for real-world chemistry and material science applications52An automatic end-to-end chemical synthesis development platform powered by large language models116Reacon: a template- and cluster-based framework for reaction condition prediction12Fundamental understanding of fluorophenol-derived dual organocatalysts for ring-opening polymerization of lactide1Computational tools for the prediction of site- and regioselectivity of organic reactions33Data-driven recommendation of agents, temperature, and equivalence ratios for organic synthesis9Electronic effects in early transition metal catalyzed olefin polymerization: challenges in featurization and descriptor strengths and weaknesses1Investigation of arene and heteroarene nitration supported by high-throughput experimentation and machine learning0Accelerating the Development of Sustainable Catalytic Processes through Data Science7Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach4Data Science-Driven Discovery of Optimal Conditions and a Condition-Selection Model for the Chan–Lam Coupling of Primary Sulfonamides17An active representation learning method for reaction yield prediction with small-scale data8C–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-Azoles1minChemBio: Expanding Chemical Synthesis with Chemo-Enzymatic Pathways Using Minimal Transitions5Bidirectional Chemical Intelligent Net: A unified deep learning–based framework for predicting chemical reaction3Advancing extrapolative predictions of material properties through learning to learn using extrapolative episodic training13Challenging Reaction Prediction Models to Generalize to Novel Chemistry23Deep-Learning-Assisted Understanding of the Self-Assembly of Miktoarm Star Block Copolymers10Unified Deep Learning of Molecular and Protein Language Representations with T5ProtChem6A meta-learning approach for selectivity prediction in asymmetric catalysis11Research progress of machine learning in the field of photocatalysis applications21Data accessibility in the chemical sciences: an analysis of recent practice in organic chemistry journals3Development of Robust, Efficient and Scalable Transition Metal Catalyzed Transformations: Translation of Reactions from Micromole to Multi-Kilogram Scale Processes23Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation32Simple Units Integration Brings New Reactivity: Neighboring Group Effect in the Synthesis of Polyfunctionalized 5‐Alkenyl‐3‐furanones from γ‐Hydroxyl Enal4ASKCOS: Open-Source, Data-Driven Synthesis Planning77Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides18Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models3Hybridised mechanistic and machine learning digital twins for modelling and optimising chemical processes in flow: A comparative analysis of parallel and series-based hybridisation12Optimal Compound Downselection To Promote Diversity and Parallel Chemistry1Optimization of Catalysts Using Computational Chemistry, Machine Learning, and Cheminformatics0Predicting Reactivity with Machine Learning1Optimizing Vanadium-Catalyzed Epoxidation Reactions: Machine-Learning-Driven Yield Predictions and Data Augmentation6Developing a Machine Learning Model for Hydrogen Bond Acceptance Based on Natural Bond Orbital Descriptors8Unveiling Unusual Reactivity of SO2 and Unusual Type of S–X Long Bonds3Discovery of Ni(I) Complexes for CO2 Insertion Enabled by a Machine Learning-Computational-Selection Sequence12Accelerated Discovery of Energy Transfer-Catalyzed Dearomative Cycloadditions through a Data-Driven Three-Layer Screening Strategy25Self-driving laboratories with artificial intelligence: An overview of process systems engineering perspective14Special Issue: Machine Learning and Data Analysis II0Electron flow matching for generative reaction mechanism prediction35Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis8Optimizing Model Learning Performance on a Challenging Heck Reaction Yield Data Set2Conformation Dependent Features of Bisphosphine Ligands5Machine Learning‐Assisted Prediction of Ground‐ and Excited‐State Redox Potentials in Iridium(III) Photocatalysts4Machine Learning‐Assisted Prediction of Ground‐ and Excited‐State Redox Potentials in Iridium(III) Photocatalysts1Artificial intelligence in chemical reaction engineering0Three-dimensional geometric deep learning for reaction prediction with equivariant graph transformer7Bond-editing driven electron flow mapping for reaction mechanism modeling0Machine learning prediction of a chemical reaction over 8 decades of energy0Advancing predictive modeling in computational chemistry through quantum chemistry, molecular mechanics, and machine learning14Decoding Regioselectivity in Cu-Catalyzed Borylation of Alkynes: Insights from Machine Learning and Artificial Intelligence2Teaching Computer-Assisted Retrosynthesis Reaction Prediction with Open-Source Software0Click Reactions: A Paradigm Shift in Food Chemical Residue Testing6An AI-Powered Molecular Prediction System for Chemical Synthesis in Digital Laboratory Workflows0Principles of Synthetic Planning0Protocol-guided evaluation of reaction yield prediction under predictable distribution shifts: a review0Collective intelligence for AI-assisted chemical synthesis21An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis6Artificial Intelligence in Drug Discovery: Integrative Advances From Data to Therapeutic Innovation13From waste rubber to value polybutadiene modification for circular materials2Staged Diversity‐Constrained Machine Learning for High‐Dimensional Reaction Condition Optimization3Stufenweises diversitätsbeschränktes maschinelles Lernen für die Optimierung hochdimensionaler Reaktionsbedingungen0Higher-Level Strategies for Computer-Aided Retrosynthesis11DAPM:Dual Path Yield Prediction Model Integrating Anti-Noise Convolution and Position Compensation0Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides0Accelerating Multi-Elemental Catalyst Discovery with Interpretable Machine Learning and Automated Experimentation0Periodic Trends in Electron-Withdrawing Ability: Effects on Carbon–Fluorine Reductive Elimination from Palladium as a Model System0Green synthesis methods: Advances, challenges, and future perspectives5From data to insight: computational tools for rationalizing organometallic and organocatalytic reactivity0Cost-Effective Quantum-Mechanical Workflows for Molecular Machine Learning4Cutting‐Edge Advances in Metal Phosphides: Rational Synthesis to Dynamic Reconstruction for Electrochemical Water Splitting3Integrating AI in Medicinal Chemistry for Accelerated Drug Discovery: A Comprehensive SAR (CSAR) Optimization Strategy and Discovery of Potent ALDH3A1 Inhibitors0Applications of AI in Organic Chemistry0Harnessing Artificial Intelligence (AI) for a greener future: a review of AI advancements in green chemistry, chemical processes and sustainable materials3Toward Rational Design of Reaction-Based Fluorescent Probes: A Reaction-Centric Perspective on Machine Learning in Chemical Sensing0AI‐Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery0Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms1ChemDraw-Guided Design of Descriptors for Quantitative Statistical Models0Pathway-Aware Template-Based Retrosynthesis1Reaction Enumeration Based on NBO‐Informed Molecular Graphs1Editorial: Machine Learning Optimization of Chemical Processes1MLIP-Enhanced Thermochemistry Predictions across Organic Chemical Space0The Role of Knowledge Representation & Reasoning in Deciphering Chemical Complexity0AI‐Assisted Waste Salt Detection: Intelligent Transformation of Reagents, Instruments, and Methods0Machine Learning-Guided Strategy Selection and Condition Prediction for Photoredox Catalysis0Toward autonomous chemical discovery: agentic AI in organic synthesis and nanochemistry0Leveraging the Synergy between Mechanistic Insight and Machine Learning for Predictive Structure–Performance Relationships in Organic Synthesis0

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  50. Machine Learned Potential Enables Molecular Dynamics Simulation to Predict the Experimental Branching Ratios in the NO Release Channel of Nitroaromatic Compounds

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