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RESEARCH PAPER · Molecular Systems Design & Engineering · 2019

Deep learning for molecular design—a review of the state of the art

Daniel C. Elton, Zois Boukouvalas, Mark Fuge, Peter W. Chung

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Deep learning for molecular design—a review of the state of the artOriginalDeep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design238Deep learning enables rapid identification of potent DDR1 kinase inhibitors1532Identification Schemes for Metal–Organic Frameworks To Enable Rapid Search and Cheminformatics Analysis247Autonomous Discovery in the Chemical Sciences Part I: Progress317Autonomous Discovery in the Chemical Sciences Part II: Outlook245Deep Convolutional Generative Adversarial Network (dcGAN) Models for Screening and Design of Small Molecules Targeting Cannabinoid Receptors80Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research57Convolutional Neural Networks for the Design and Analysis of Non-Fullerene Acceptors56Constrained Bayesian optimization for automatic chemical design using variational autoencoders339Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns181On failure modes in molecule generation and optimization154Attribute driven inverse materials design using deep learning Bayesian framework62Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future211Machine Learning Discovery of High-Temperature Polymers133Machine learning the ropes: principles, applications and directions in synthetic chemistry279Machine-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 intelligence419Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges191Exploring chemical space using natural language processing methodologies for drug discovery160ChEMBL-Likeness Score and Database GDBChEMBL62A Structure-Based Platform for Predicting Chemical Reactivity288Deep Generative Models for 3D Linker Design258In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways169The power of deep learning to ligand-based novel drug discovery76Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery99CReM: chemically reasonable mutations framework for structure generation112A Review of Deep Learning Methods for Antibodies72Machine Learning on Graphs: A Model and Comprehensive Taxonomy92kGCN: a graph-based deep learning framework for chemical structures106Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks234Artificial intelligence in chemistry and drug design165Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Machine learning and excited-state molecular dynamics62Role of Artificial Intelligence and Machine Learning in Nanosafety175Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials295Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions120The Advent of Generative Chemistry145Electronic Structure Modeling of Metal–Organic Frameworks329Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design109Transfer Learning for Drug Discovery366Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN61Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation660Chemical space exploration: how genetic algorithms find the needle in the haystack51Deep computational pathology in breast cancer85Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging65EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation75Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications82PubChemQC PM6: Data Sets of 221 Million Molecules with Optimized Molecular Geometries and Electronic Properties79Structure prediction of crystals, surfaces and nanoparticles54Machine Learning for Electronically Excited States of Molecules398Uncertainty quantification in drug design95Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches131Graph networks for molecular design183Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently55Scaffold-Constrained Molecular Generation64Defining and Exploring Chemical Spaces124High-Throughput Molecular Dynamics Simulations and Validation of Thermophysical Properties of Polymers for Various Applications124A review on machine learning algorithms for the ionic liquid chemical space196Data-driven algorithms for inverse design of polymers100Physically inspired deep learning of molecular excitations and photoemission spectra65TSNet: predicting transition state structures with tensor field networks and transfer learning62Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases62Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions91Transformer neural network for protein-specific de novo drug generation as a machine translation problem222Machine learning for molecular thermodynamics65Discovery of novel chemical reactions by deep generative recurrent neural network81Generative chemistry: drug discovery with deep learning generative models171Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks52Computational catalysis for metal-organic frameworks: An overview67Artificial Intelligence and Early Detection of Pancreatic Cancer157Diversity oriented Deep Reinforcement Learning for targeted molecule generation82SMILES Pair Encoding: A Data-Driven Substructure Tokenization Algorithm for Deep Learning133Deep Learning Optical Spectroscopy Based on Experimental Database: Potential Applications to Molecular Design170MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES61Artificial intelligence in drug discovery: recent advances and future perspectives458Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks105Critical assessment of AI in drug discovery106Active discovery of organic semiconductors135Inferring experimental procedures from text-based representations of chemical reactions80Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study76Masked graph modeling for molecule generation93De novo molecular design and generative models292Perspective on integrating machine learning into computational chemistry and materials science203Reinforcement learning applied to metamaterial design54Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations68Artificial Intelligence in Chemistry: Current Trends and Future Directions286Chemical language models enable navigation in sparsely populated chemical space167Molecular design in drug discovery: a comprehensive review of deep generative models169Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers61Artificial intelligence in drug discovery: applications and techniques220Application advances of deep learning methods for de novo drug design and molecular dynamics simulation155Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning191Bayesian optimization for chemical products and functional materials172Generative Deep Learning for Targeted Compound Design191Predicting Antimicrobial Activity of Conjugated Oligoelectrolyte Molecules via Machine Learning57High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research140A deep generative model enables automated structure elucidation of novel psychoactive substances86MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra87Artificial intelligence: machine learning for chemical sciences104Molecular design of redox carriers for electrochemical CO 2 capture and concentration81Simulation of deep eutectic solvents: Progress to promises86Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design59X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis66Towards artificial intelligence at scale in the chemical industry69A review of molecular representation in the age of machine learning368Inverse design of 3d molecular structures with conditional generative neural networks188Design of potent antimalarials with generative chemistry57Compound–protein interaction prediction by deep learning: Databases, descriptors and models73Generative models for molecular discovery: Recent advances and challenges318Machine intelligence for chemical reaction space124Machine Learning–Assisted Design of Material Properties80Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation118Data‐Driven Materials Innovation and Applications187AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2144MSNovelist: de novo structure generation from mass spectra221Fragment‐based drug discovery—the importance of high‐quality molecule libraries150Design, Synthesis, Docking, DFT, MD Simulation Studies of a New Nicotinamide-Based Derivative: In Vitro Anticancer and VEGFR-2 Inhibitory Effects121A Generative Approach to Materials Discovery, Design, and Optimization121Graph representation learning in biomedicine and healthcare287Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery138Graph Neural Networks for Molecules84A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.076Chemical language models for de novo drug design: Challenges and opportunities126High-throughput property-driven generative design of functional organic molecules71Integrating structure-based approaches in generative molecular design57Regression Transformer enables concurrent sequence regression and generation for molecular language modelling141Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark69Inverse design and AI/Deep generative networks in food design: A comprehensive review59APPROPRIATE Life Cycle Assessment: A PRO cess-Specific, PR edictive I mpact A ssessmen T Method for Emerging Chemical Processes55Towards artificial intelligence-enabled extracellular vesicle precision drug delivery154Open-Source Machine Learning in Computational Chemistry59DDI-GCN: Drug-drug interaction prediction via explainable graph convolutional networks81PubChemQC B3LYP/6-31G*//PM6 Data Set: The Electronic Structures of 86 Million Molecules Using B3LYP/6-31G* Calculations64Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy149Discovery of antibiotics that selectively kill metabolically dormant bacteria55Advances in Artificial Intelligence (AI)-assisted approaches in drug screening136Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning70MedGAN: optimized generative adversarial network with graph convolutional networks for novel molecule design72AI methods in materials design, discovery and manufacturing: A review142Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities189Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery279Invalid SMILES are beneficial rather than detrimental to chemical language models65Prospective de novo drug design with deep interactome learning96Unleashing the power of generative AI in drug discovery187Concepts and applications of digital twins in healthcare and medicine149Diffusion Models in De Novo Drug Design116Review of machine learning for lipid nanoparticle formulation and process development61Advancing materials discovery through artificial intelligence68Nanomaterials in Drug Delivery: Leveraging Artificial Intelligence and Big Data for Predictive Design56

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