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RESEARCH PAPER · Nature Reviews Chemistry · 2019

How to explore chemical space using algorithms and automation

Piotr S. Gromski, Alon Henson, Jarosław M. Granda, Leroy Cronin

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How to explore chemical space using algorithms and automationOriginalEstudios, Contrapunto Callas-Gencer: Crítica discográfica0Deep learning for molecular design—a review of the state of the art677Electrospray and Nanoelectrospray Ionization-Mass Spectrometry Systems for the High-Throughput Analysis of Microfluidic Droplets0Identifying high-performance catalytic conditions for carbon dioxide reduction to dimethoxymethane by multivariate modelling32Algorithm-supported, mass and sequence diversity-oriented random peptide library design15Kinetics Studies on a Multicomponent Knoevenagel−Michael Domino Reaction by an Automated Flow Reactor14Designing materials for electrochemical carbon dioxide recycling1293Machine learning approaches for drug virtual screening0Universal Chemical Synthesis and Discovery with ‘The Chemputer’154Synthetic organic chemistry driven by artificial intelligence303One-Pot Parallel Synthesis of 5-(Dialkylamino)tetrazoles13Machine Learning Interatomic Potentials as Emerging Tools for Materials Science1036Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonome Entdeckung in den chemischen Wissenschaften, Teil II: Ausblick4Autonomous Discovery in the Chemical Sciences Part I: Progress317Autonomous Discovery in the Chemical Sciences Part II: Outlook245Reconfigurable Flow Platform for Automated Reagent Screening and Autonomous Optimization for Bioinspired Lignans Synthesis35Machine learning for target discovery in drug development61A streamlined synthesis of the neurosteroid 3β-methoxypregnenolone assisted by a statistical experimental design and automation6Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns181Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Enabling Catalyst Discovery through Machine Learning and High-Throughput Experimentation133A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes27Automated generation of photochemical reaction data by transient flow experiments coupled with online HPLC analysis26Designing peptide nanoparticles for efficient brain delivery52Digitising chemical synthesis in automated and robotic flow49Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex178Optical monitoring of polymerizations in droplets with high temporal dynamic range24Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation618A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation131Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges191Toward “On‐Demand” Materials Synthesis and Scientific Discovery through Intelligent Robots76The Medicinal Chemistry in the Era of Machines and Automation: Recent Advances in Continuous Flow Technology150Design of an Automated Reagent-Dispensing System for Reaction Screening and Validation with DNA-Tagged Substrates20Next generation 3D pharmacophore modeling269Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis243Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab171SYBA: Bayesian estimation of synthetic accessibility of organic compounds149A Fast and Reliable Screening Setup for Homogeneous Catalysis with Gaseous Reactants at Extreme Temperatures and Pressures5Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Elucidating the Weakly Reversible Cs–Pb–Br Perovskite Nanocrystal Reaction Network with High-Throughput Maps and Transformations73Molecular Printing with DNA Nanotechnology30Comparing molecular dynamics-derived pharmacophore models with docking: A study on CDK-2 inhibitors14The Advent of Generative Chemistry145Artificial Intelligence Applied to the Prediction of Organic Materials12Machine Learning for Polymer Swelling in Liquids34Bioactivity descriptors for uncharacterized compounds6Exploring Reactivity and Regioselectivity of Dimerization of Paramagnetic Endohedral Metallofullerenes5Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation660Automating Academic Laboratories: Promoting Reliability, Productivity, and Safety7Flow Chemistry As a Tool to Access Novel Chemical Space for Drug Discovery9A Mild Method for Making MIDA Boronates22Mapping the resources and approaches facilitating computer-aided synthesis planning37The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal Complexes11Digital Reticular Chemistry171A Reactivity First Approach to Autonomous Discovery of New Chemistry0Genetic Algorithm Parametrization for Informed Exploration of Short Peptides Chemical Space2Computer-inspired quantum experiments86tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes138The Role of Machine Learning in the Understanding and Design of Materials433Machine Learning Methods in Drug Discovery456Microfluidic Synthesis of Semiconductor Materials: Toward Accelerated Materials Development in Flow41Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine189A droplet microfluidic platform for high-throughput photochemical reaction discovery172Microplotter-Printed On-Chip Combinatorial Library of Ink-Derived Multiple Metal Oxides as an “Electronic Olfaction” Unit47Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently55Self‐Driven Multistep Quantum Dot Synthesis Enabled by Autonomous Robotic Experimentation in Flow95Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning9Investigating Active Learning and Meta-Learning for Iterative Peptide Design34Accelerated AI development for autonomous materials synthesis in flow68Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments17Golem: an algorithm for robust experiment and process optimization38Scientific intuition inspired by machine learning-generated hypotheses35Scientific intuition inspired by machine learning-generated hypotheses31Machine learning for molecular thermodynamics65The shape of porphyrins255TIES 20: Relative Binding Free Energy with a Flexible Superimposition Algorithm and Partial Ring Morphing25Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality56Self‐Driven Multistep Quantum Dot Synthesis Enabled by Autonomous Robotic Experimentation in Flow31Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery122A Unifying Review of Deep and Shallow Anomaly Detection914Introduction to Machine Learning for Chemists: An Undergraduate Course Using Python Notebooks for Visualization, Data Processing, Data Analysis, and Data Modeling0Chemical Space Mapping for Multicomponent Gas Mixtures1TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes0TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes0Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation1Organic reactivity from mechanism to machine learning175Quantitative Prediction on the Enantioselectivity of Multiple Chiral Iodoarene Scaffolds Based on Whole Geometry0Diversity-oriented synthesis of polymer membranes with ion solvation cages143A Reactivity First Approach to Autonomous Discovery of New Chemistry0Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project9Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches72Autonomous flow reactors for discovery and invention29Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts77Rapid Optimization of Photoredox Reactions for Continuous-Flow Systems Using Microscale Batch Technology94Bioactivity descriptors for uncharacterized chemical compounds92Chemical space mapping for multicomponent gas mixtures11Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Machine Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis3Trends in Deep Learning for Property-driven Drug Design31MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning15Microfluidic Studies of Cesium Lead Halide Perovskite Quantum Dots.0Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning78Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning6A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling88Design and Application of a Screening Set for Monophosphine Lig-ands in Metal Catalysis7Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments46Chemputation and the Standardization of Chemical Informatics73Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models44New trends in nonconventional carbon dot synthesis602D Molybdenum Carbide MXenes for Enhanced Selective Detection of Humidity in Air100Machine-Learning-Guided Discovery of 19 F MRI Agents Enabled by Automated Copolymer Synthesis155Enabling modular autonomous feedback-loops in materials science through hierarchical experimental laboratory automation and orchestration5Recent advances in autonomous synthesis of materials33Self-reproducing catalytic micelles as nanoscopic protocell precursors82Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives21Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network76Self-Optimization of Continuous Flow Electrochemical Synthesis Using Fourier Transform Infrared Spectroscopy and Gas Chromatography15Chemical Reactivity Prediction: Current Methods and Different Application Areas17Accelerating colloidal quantum dot innovation with algorithms and automation30Artificial intelligence–enabled fuel design2Interactions Between Systems, Their Differences and Environments0Introduction0Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors39Machine learning enabling high-throughput and remote operations at large-scale user facilities16Methods0Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration73Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction toward Extrapolative Prediction of Novel Catalysts25MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**237Vorhersage der MOF‐Synthese durch automatisches Data‐Mining und maschinelles Lernen**4Prediction of Compound Synthesis Accessibility Based on Reaction Knowledge Graph21Application of Advanced Vibrational Spectroscopy in Revealing Critical Chemical Processes and Phenomena of Electrochemical Energy Storage and Conversion27Deep Evolutionary Learning for Molecular Design37CENT2: Improved charge equilibration via neural network technique28Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives31Evaluation guidelines for machine learning tools in the chemical sciences180GraphRXN: A Novel Representation for Reaction Prediction0Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides37Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling57Integrated Systems for Continuous Synthesis and Biological Screenings3Predicting Real‐Time Spectra from High‐Speed Imaging: An Ultrafast Machine Vision Framework for Online Optical Control in Microfluidics1Curiosity in exploring chemical spaces: intrinsic rewards for molecular reinforcement learning20Graph-based Automated Macro-Molecule Assembly0Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space19Advancing molecular graphs with descriptors for the prediction of chemical reaction yields20Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery138DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space26

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