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RESEARCH PAPER · Science · 2018

Inverse molecular design using machine learning: Generative models for matter engineering

Benjamín Sánchez-Lengeling, Alán Aspuru‐Guzik

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Inverse molecular design using machine learning: Generative models for matter engineeringOriginalIntelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale302Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery270Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry190Deep Learning for Nonadiabatic Excited-State Dynamics172A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space352A quantitative uncertainty metric controls error in neural network-driven chemical discovery240Artificial Intelligence and Personalized Medicine488Machine learning enables long time scale molecular photodynamics simulations214Machine learning for renewable energy materials312Progress and prospects for accelerating materials science with automated and autonomous workflows228Solving the electronic structure problem with machine learning314From DFT to machine learning: recent approaches to materials science–a review849Recent advances in machine learning towards multiscale soft materials design173Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming343Deep learning in bioinformatics: Introduction, application, and perspective in the big data era397Toward Design of Novel Materials for Organic Electronics167Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists208Deep Learning in Chemistry561Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design238Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm478Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy615Advancing Drug Discovery via Artificial Intelligence649Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network277Synthetic organic chemistry driven by artificial intelligence303Deep learning enables rapid identification of potent DDR1 kinase inhibitors1532Machine learning in materials science1058Autonomous Discovery in the Chemical Sciences Part I: Progress317Autonomous Discovery in the Chemical Sciences Part II: Outlook245Inverse Design of Solid-State Materials via a Continuous Representation379Constrained Bayesian optimization for automatic chemical design using variational autoencoders339Designing phononic crystal with anticipated band gap through a deep learning based data-driven method256Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences639Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns181Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening310Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex178Machine learning-driven new material discovery296Machine-enabled inverse design of inorganic solid materials: promises and challenges190Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation618De novo generation of hit-like molecules from gene expression signatures using artificial intelligence419Inverse design of porous materials using artificial neural networks354Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges191Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning335Automated in Silico Design of Homogeneous Catalysts186Advances in Conjugated Microporous Polymers1394Multiscale modeling meets machine learning: What can we learn?353Machine learning: Accelerating materials development for energy storage and conversion370Machine Learning for Molecular Simulation906The Exploration of Chemical Reaction Networks202Extraction of mechanical properties of materials through deep learning from instrumented indentation352The Synthesizability of Molecules Proposed by Generative Models394Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics194Synthetic promoter design in Escherichia coli based on a deep generative network208Learning Molecular Representations for Medicinal Chemistry222Opportunities and Challenges for Machine Learning in Materials Science523Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks234The prospects of quantum computing in computational molecular biology215Artificial intelligence in chemistry and drug design165Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599The 2020 magnetism roadmap220Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials295Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation660Applications of machine learning in metal-organic frameworks225Molecular representations in AI-driven drug discovery: a review and practical guide578Deep learning for the design of photonic structures1136Material Design of Aqueous Redox Flow Batteries: Fundamental Challenges and Mitigation Strategies256Integrating data mining and machine learning to discover high-strength ductile titanium alloys190Machine Learning for Advanced Additive Manufacturing270Emerging materials intelligence ecosystems propelled by machine learning441The Role of Machine Learning in the Understanding and Design of Materials433Deep Learning in Protein Structural Modeling and Design226Machine Learning for Electronically Excited States of Molecules398Machine learning for high performance organic solar cells: current scenario and future prospects375Graph networks for molecular design183Variational Quantum Generators: Generative Adversarial Quantum Machine Learning for Continuous Distributions173Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models828Applications of Deep Learning in Molecule Generation and Molecular Property Prediction382Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES165Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys259Machine learning of solvent effects on molecular spectra and reactions174Physics-Inspired Structural Representations for Molecules and Materials431Machine learned features from density of states for accurate adsorption energy prediction257Tackling Photonic Inverse Design with Machine Learning211Inverse design of nanoporous crystalline reticular materials with deep generative models400Data-Driven Strategies for Accelerated Materials Design473Generative chemistry: drug discovery with deep learning generative models171Machine learning in materials science: From explainable predictions to autonomous design281Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations414Deep Learning Optical Spectroscopy Based on Experimental Database: Potential Applications to Molecular Design170Fusing Stretchable Sensing Technology with Machine Learning for Human–Machine Interfaces164How Machine Learning Will Revolutionize Electrochemical Sciences191Machine learning directed drug formulation development335Molecular excited states through a machine learning lens200Machine-learned potentials for next-generation matter simulations560Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review207Best practices in machine learning for chemistry505De novo molecular design and generative models292Combining generative artificial intelligence and on-chip synthesis for de novo drug design187Perspective on integrating machine learning into computational chemistry and materials science203Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale166Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems840Nanoparticle synthesis assisted by machine learning545Application of machine learning algorithms in municipal solid waste management: A mini review240Chemical language models enable navigation in sparsely populated chemical space167Physics-Inspired Structural Representations for Molecules and Materials523Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats323Ab Initio Machine Learning in Chemical Compound Space195Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage260Deep learning framework for material design space exploration using active transfer learning and data augmentation178Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries412Accelerating antibiotic discovery through artificial intelligence227Artificial Intelligence Applied to Battery Research: Hype or Reality?503Generative Models for De Novo Drug Design233The Evolution of Data-Driven Modeling in Organic Chemistry185MolGPT: Molecular Generation Using a Transformer-Decoder Model543Generative Deep Learning for Targeted Compound Design191Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data198Natural product drug discovery in the artificial intelligence era219A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis309Inverse design of 3d molecular structures with conditional generative neural networks188Generative models for molecular discovery: Recent advances and challenges318Artificial intelligence to bring nanomedicine to life189Data‐Driven Materials Innovation and Applications187Applying Classical, Ab Initio , and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries562Evaluation guidelines for machine learning tools in the chemical sciences180Challenges and opportunities in quantum machine learning689Machine learning for high-entropy alloys: Progress, challenges and opportunities397Artificial intelligence foundation for therapeutic science199SELFIES and the future of molecular string representations261Machine learning for a sustainable energy future517Graph neural networks for materials science and chemistry86014 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon214Intelligent Computing: The Latest Advances, Challenges, and Future290Covalent organic frameworks814Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Generative Models as an Emerging Paradigm in the Chemical Sciences313Generative artificial intelligence and its applications in materials science: Current situation and future perspectives293AI in drug discovery and its clinical relevance299Can enterprise green technology innovation performance achieve “corner overtaking” by using artificial intelligence?—Evidence from Chinese manufacturing enterprises251Scientific discovery in the age of artificial intelligence1779Applied machine learning as a driver for polymeric biomaterials design167Machine learning-enabled constrained multi-objective design of architected materials172Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling209Scope of machine learning in materials research—A review230Leveraging large language models for predictive chemistry367Self-Driving Laboratories for Chemistry and Materials Science674Data Generation for Machine Learning Interatomic Potentials and Beyond176Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials188A generative model for inorganic materials design498Integrating artificial intelligence in drug discovery and early drug development: a transformative approach187

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