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RESEARCH PAPER · Journal of Computational Chemistry · 2017

Deep learning for computational chemistry

Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu

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42 direct citing works

Deep learning for computational chemistryOriginalChemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models111Automating drug discovery895Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment553Machine learning material properties from the periodic table using convolutional neural networks115Towards operando computational modeling in heterogeneous catalysis250The rise of deep learning in drug discovery1751In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts280How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?71Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics401Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era351Opportunities and obstacles for deep learning in biology and medicine2290Computational Protein Design with Deep Learning Neural Networks166Advancing drug discovery via GPU-based deep learning72Accelerating the discovery of materials for clean energy in the era of smart automation776Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification176Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials925Crystal Structure Prediction via Deep Learning355Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases529Recent applications of machine learning in medicinal chemistry129Designing Algorithms To Aid Discovery by Chemical Robots78Conditional Molecular Design with Deep Generative Models197Using Rule-Based Labels for Weak Supervised Learning85DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning93Artificial Intelligence in Drug Discovery173Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction152Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering346A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists1133Deep learning to represent subgrid processes in climate models1023A review on machine learning methods forin silicotoxicity prediction144Artificial Intelligence in Drug Design373Deep learning for predicting toxicity of chemicals: a mini review84Neural Networks for Postprocessing Ensemble Weather Forecasts509Accelerated Computation of Free Energy Profile at ab Initio Quantum Mechanical/Molecular Mechanics Accuracy via a Semi-Empirical Reference Potential. I. Weighted Thermodynamics Perturbation67A Review of Ligand-Based Virtual Screening Web Tools and Screening Algorithms in Large Molecular Databases in the Age of Big Data96HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community335QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery494Kinetic energy densities based on the fourth order gradient expansion: performance in different classes of materials and improvement via machine learning70Predicting tumor cell line response to drug pairs with deep learning130Deep convolutional neural networks for eigenvalue problems in mechanics78Remote Sensing: An Advanced Technique for Crop Condition Assessment66The Impact of Artificial Intelligence on the Labor Market467Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines112Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets222DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks245Nanomaterials Discovery and Design through Machine Learning113Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models86Machine learning for composite materials311Automated Stoichiometry Analysis of Single-Molecule Fluorescence Imaging Traces via Deep Learning78Deep learning in bioinformatics: Introduction, application, and perspective in the big data era397Toward Design of Novel Materials for Organic Electronics167Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization80DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks156Gas separation by adsorption: technological drivers and opportunities for improvement123HyperPNN: Hyperspectral Pansharpening via Spectrally Predictive Convolutional Neural Networks134Deep Learning in Chemistry561Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks198Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy615Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network175Deep learning in drug discovery: opportunities, challenges and future prospects350Prediction and optimization of mechanical properties of composites using convolutional neural networks239Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology65The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation123Global discovery of stable and non-toxic hybrid organic-inorganic perovskites for photovoltaic systems by combining machine learning method with first principle calculations99Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks95Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests91Open-source QSAR models for pKa prediction using multiple machine learning approaches177Autonomous Discovery in the Chemical Sciences Part I: Progress317Artificial Intelligence in Steam Cracking Modeling: A Deep Learning Algorithm for Detailed Effluent Prediction73Applying Deep Learning to the Cache Replacement Problem139Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies153Toward the inverse design of MOF membranes for efficient D2/H2 separation by combination of physics-based and data-driven modeling72Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation78Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns181Deep learning for drug response prediction in cancer237Graph Convolutional Neural Networks as “General-Purpose” Property Predictors: The Universality and Limits of Applicability93Design and Evaluation of a Deep Learning Recommendation Based Augmented Reality System for Teaching Programming and Computational Thinking127Machine learning-driven new material discovery296Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworks158Automated in Silico Design of Homogeneous Catalysts186Prediction of composite microstructure stress-strain curves using convolutional neural networks446Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction74A Critical Review of Machine Learning of Energy Materials606Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning76Dinucleoside polyphosphates act as 5′-RNA caps in bacteria66Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation78DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNet78An Analysis of QSAR Research Based on Machine Learning Concepts96Applications of smart proxies for subsurface modeling69Chalcogenides by Design: Functionality through Metavalent Bonding and Confinement310Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction65Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review80A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra100kGCN: a graph-based deep learning framework for chemical structures106Recent advances in knowledge discovery for heterogeneous catalysis using machine learning130A data-efficient self-supervised deep learning model for design and characterization of nanophotonic structures75Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions120Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades212Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning75Operable adaptive sparse identification of systems: Application to chemical processes98Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design109Physics-Based Approach for a Neural Networks Enabled Design of All-Dielectric Metasurfaces74Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods66Deep learning for the design of photonic structures1136TrimNet: learning molecular representation from triplet messages for biomedicine101Machine learning toward advanced energy storage devices and systems199Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning83Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models120A review on machine learning algorithms for the ionic liquid chemical space196Tackling Photonic Inverse Design with Machine Learning211Progress in the Theory of X-ray Spectroscopy: From Quantum Chemistry to Machine Learning and Ultrafast Dynamics78Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data292Review of deep learning: concepts, CNN architectures, challenges, applications, future directions7842Artificial intelligence to deep learning: machine intelligence approach for drug discovery1455Big data and machine learning for materials science180Enhanced Harris hawks optimization with genetic operators for selection chemical descriptors and compounds activities71Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles299Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems840Machine Learning Boosting the Development of Advanced Lithium Batteries68The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning155Bioactive Synthetic Polymers183The Evolution of Data-Driven Modeling in Organic Chemistry185Enhancing preclinical drug discovery with artificial intelligence180Machine Learning and Deep Learning in Data-Driven Decision Making of Drug Discovery and Challenges in High-Quality Data Acquisition in the Pharmaceutical Industry69Molecular representations for machine learning applications in chemistry93Machine learning activation energies of chemical reactions83Sigma profiles in deep learning: towards a universal molecular descriptor67Benchmarking machine learning methods for modeling physical properties of ionic liquids70Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design285De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update72On the application of physics informed neural networks (PINN) to solve boundary layer thermal-fluid problems140Inverse Design of Materials by Machine Learning137Deep learning object detection in materials science: Current state and future directions82GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction114Protein–Ligand Docking in the Machine-Learning Era188Cross‐validated permutation feature importance considering correlation between features136Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond497Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling190Machine learning and deep learning in phononic crystals and metamaterials – A review117A review of recent advances and applications of machine learning in tribology109Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures73A Perspective on Explanations of Molecular Prediction Models107Generative Models as an Emerging Paradigm in the Chemical Sciences313Discovery of senolytics using machine learning131An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice205Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective212Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications86Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis336Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms79Role of artificial intelligence in revolutionizing drug discovery134From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design190

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