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RESEARCH PAPER · Cambridge University Engineering Department Publications Database · 2018

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.

Rafael Gómez‐Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera‐Iparraguirre, Timothy D. Hirzel, Ryan Prescott Adams, Alán Aspuru‐Guzik

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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.OriginalSMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules273Low Data Drug Discovery with One-Shot Learning787Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models413Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction469Molecular de-novo design through deep reinforcement learning1324Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models534Generative Recurrent Networks for De Novo Drug Design441ChemTS: an efficient python library for de novo molecular generation262TensorFlow Distributions226Optimizing Chemical Reactions with Deep Reinforcement Learning437Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks1528“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models441Large-scale comparison of machine learning methods for drug target prediction on ChEMBL578Towards operando computational modeling in heterogeneous catalysis250The rise of deep learning in drug discovery1751Junction Tree Variational Autoencoder for Molecular Graph Generation720Learning Deep Generative Models of Graphs385Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics401Machine learning in catalysis479Deep learning improves prediction of drug–drug and drug–food interactions655Accelerating the discovery of materials for clean energy in the era of smart automation776Machine Learning in Computer-Aided Synthesis Planning703Development and evaluation of a deep learning model for protein–ligand binding affinity prediction663Constrained Graph Variational Autoencoders for Molecule Design295Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation226Controlling an organic synthesis robot with machine learning to search for new reactivity740Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors258DeepDTA: deep drug–target binding affinity prediction1696Artificial intelligence in drug design259Multi-objective de novo drug design with conditional graph generative model376Inverse molecular design using machine learning: Generative models for matter engineering2050Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery343Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine226Phoenics: A Bayesian Optimizer for Chemistry373Deep learning in biomedicine585Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery270Deep generative models of genetic variation capture the effects of mutations597Artificial Intelligence in Drug Design373Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities423Exascale Deep Learning for Climate Analytics278Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations510Artificial intelligence for aging and longevity research: Recent advances and perspectives245Differentiable learning of quantum circuit Born machines279A 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 discovery240Deep learning for molecular design—a review of the state of the art677Efficient multi-objective molecular optimization in a continuous latent space260Machine learning for renewable energy materials312Progress and prospects for accelerating materials science with automated and autonomous workflows228How to explore chemical space using algorithms and automation245Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning678Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra262Shape-Based Generative Modeling for de Novo Drug Design228Next-Generation Experimentation with Self-Driving Laboratories421Graph convolutional networks for computational drug development and discovery413Deep 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 algorithm478Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery1022Analyzing Learned Molecular Representations for Property Prediction1902Deep learning in drug discovery: opportunities, challenges and future prospects350Deep learning enables rapid identification of potent DDR1 kinase inhibitors1532Machine learning in materials science1058Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning744Machine Learning Interatomic Potentials as Emerging Tools for Materials Science1036BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules322Identification Schemes for Metal–Organic Frameworks To Enable Rapid Search and Cheminformatics Analysis247Autonomous Discovery in the Chemical Sciences Part II: Outlook245Inverse Design of Solid-State Materials via a Continuous Representation379Unified rational protein engineering with sequence-based deep representation learning1121Data-driven modeling and learning in science and engineering296Constrained Bayesian optimization for automatic chemical design using variational autoencoders339An Introduction to Variational Autoencoders2577A de novo molecular generation method using latent vector based generative adversarial network402Rethinking drug design in the artificial intelligence era880DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations240Machine learning the ropes: principles, applications and directions in synthetic chemistry279Self-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 networks354Bidirectional Molecule Generation with Recurrent Neural Networks229A Critical Review of Machine Learning of Energy Materials606Assessing the impact of generative AI on medicinal chemistry257In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery240A Structure-Based Platform for Predicting Chemical Reactivity288Transformer-CNN: Swiss knife for QSAR modeling and interpretation262Deep Generative Models for 3D Linker Design258A Comprehensive Survey on Graph Neural Networks2955The Synthesizability of Molecules Proposed by Generative Models394The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules280Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks234Discovering Anti-Cancer Drugs via Computational Methods307Inverse-designed spinodoid metamaterials444Exploring chemical compound space with quantum-based machine learning369Machine learning approaches to drug response prediction: challenges and recent progress425Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens441MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm287Generative Adversarial Networks for Crystal Structure Prediction265Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling228Machine learning approaches for the prediction of materials properties255Polymer design using genetic algorithm and machine learning251ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction379Invited review: Machine learning for materials developments in metals additive manufacturing231REINVENT 2.0: An AI Tool for De Novo Drug Design487The Role of Machine Learning in the Understanding and Design of Materials433Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions426Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models828Applications of Deep Learning in Molecule Generation and Molecular Property Prediction382Synthetic Data for Deep Learning423Inverse design of nanoporous crystalline reticular materials with deep generative models400Data-Driven Strategies for Accelerated Materials Design473Advances in De Novo Drug Design: From Conventional to Machine Learning Methods353Machine learning in materials science: From explainable predictions to autonomous design281Artificial intelligence in drug discovery: recent advances and future perspectives458Deep learning model to predict complex stress and strain fields in hierarchical composites389Machine-learned potentials for next-generation matter simulations560Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems840Nanoparticle synthesis assisted by machine learning545Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats323Machine Learning in Drug Discovery: A Review792Artificial intelligence in cancer research, diagnosis and therapy263Generative Models for De Novo Drug Design233Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature246MolGPT: Molecular Generation Using a Transformer-Decoder Model543Geometric deep learning on molecular representations373Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting309Molecular contrastive learning of representations via graph neural networks851Recent advances and applications of deep learning methods in materials science1161GEOM, energy-annotated molecular conformations for property prediction and molecular generation264SELFIES and the future of molecular string representations261Machine learning for a sustainable energy future517Deep generative molecular design reshapes drug discovery290Graph representation learning in biomedicine and healthcare287Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development343Artificial intelligence for drug discovery: Resources, methods, and applications273Generative Models as an Emerging Paradigm in the Chemical Sciences313The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies683AI in drug discovery and its clinical relevance299Machine Learning Methods for Small Data Challenges in Molecular Science499ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis536Artificial intelligence for natural product drug discovery460Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis336Chemprop: A Machine Learning Package for Chemical Property Prediction663Machine learning for functional protein design270Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering246Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations320Reinvent 4: Modern AI–driven generative molecule design319Augmenting large language models with chemistry tools762Self-Driving Laboratories for Chemistry and Materials Science674

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  8. Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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  9. Prediction of p K a Values for Druglike Molecules Using Semiempirical Quantum Chemical Methods

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  13. SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules

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  14. Low Data Drug Discovery with One-Shot Learning

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  16. Molecular De Novo Design through Deep Reinforcement Learning

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  25. Molecular de-novo design through deep reinforcement learning

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  26. Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

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  27. Constrained Bayesian Optimization for Automatic Chemical Design

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  28. Efficient hyperparameter optimization by using Bayesian optimization for drug-target interaction prediction

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  29. Generative Recurrent Networks for De Novo Drug Design

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  45. Big Data in Drug Discovery

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  49. Machine Learning and Big-Data in Computational Chemistry

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