标题
Deep learning for computational chemistry
作者
关键词
-
出版物
JOURNAL OF COMPUTATIONAL CHEMISTRY
Volume 38, Issue 16, Pages 1291-1307
出版商
Wiley
发表日期
2017-03-08
DOI
10.1002/jcc.24764
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- A simple model predicts UGT-mediated metabolism
- (2016) Na Le Dang et al. BIOINFORMATICS
- Applications of Deep Learning in Biomedicine
- (2016) Polina Mamoshina et al. MOLECULAR PHARMACEUTICS
- Mastering the game of Go with deep neural networks and tree search
- (2016) David Silver et al. NATURE
- Machine-learning-assisted materials discovery using failed experiments
- (2016) Paul Raccuglia et al. NATURE
- Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network
- (2016) Tyler B. Hughes et al. ACS Central Science
- Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery
- (2015) Edward O. Pyzer-Knapp et al. ADVANCED FUNCTIONAL MATERIALS
- Site of Reactivity Models Predict Molecular Reactivity of Diverse Chemicals with Glutathione
- (2015) Tyler B. Hughes et al. CHEMICAL RESEARCH IN TOXICOLOGY
- ImageNet Large Scale Visual Recognition Challenge
- (2015) Olga Russakovsky et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
- (2015) O. Anatole von Lilienfeld et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Crystal structure representations for machine learning models of formation energies
- (2015) Felix Faber et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Improved Prediction of CYP-Mediated Metabolism with Chemical Fingerprints
- (2015) Jed Zaretzki et al. Journal of Chemical Information and Modeling
- Deep Learning for Drug-Induced Liver Injury
- (2015) Youjun Xu et al. Journal of Chemical Information and Modeling
- Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships
- (2015) Junshui Ma et al. Journal of Chemical Information and Modeling
- Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations
- (2015) Pavlo O. Dral et al. Journal of Chemical Theory and Computation
- Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
- (2015) Raghunathan Ramakrishnan et al. Journal of Chemical Theory and Computation
- Accurate Modeling of Ionic Surfactants at High Concentration
- (2015) Garrett B. Goh et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Deep Learning in Drug Discovery
- (2015) Erik Gawehn et al. Molecular Informatics
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- (2015) Babak Alipanahi et al. NATURE BIOTECHNOLOGY
- Big–deep–smart data in imaging for guiding materials design
- (2015) Sergei V. Kalinin et al. NATURE MATERIALS
- Deep learning in neural networks: An overview
- (2015) Jürgen Schmidhuber NEURAL NETWORKS
- A deep learning framework for modeling structural features of RNA-binding protein targets
- (2015) Sai Zhang et al. NUCLEIC ACIDS RESEARCH
- Artificial evolution of coumarin dyes for dye sensitized solar cells
- (2015) Vishwesh Venkatraman et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Accurate and efficient target prediction using a potency-sensitive influence-relevance voter
- (2015) Alessandro Lusci et al. Journal of Cheminformatics
- Improving prediction of secondary structure, local backbone angles and solvent accessible surface area of proteins by iterative deep learning
- (2015) Rhys Heffernan et al. Scientific Reports
- Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network
- (2015) Tyler B. Hughes et al. ACS Central Science
- Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network
- (2014) James Lyons et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Modeling electronic quantum transport with machine learning
- (2014) Alejandro Lopez-Bezanilla et al. PHYSICAL REVIEW B
- Searching for exotic particles in high-energy physics with deep learning
- (2014) P. Baldi et al. Nature Communications
- Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway
- (2014) Ruili Huang et al. Scientific Reports
- Stalking the Materials Genome: A Data-Driven Approach to the Virtual Design of Nanostructured Polymers
- (2013) Curt M. Breneman et al. ADVANCED FUNCTIONAL MATERIALS
- Representation Learning: A Review and New Perspectives
- (2013) Y. Bengio et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules
- (2013) Alessandro Lusci et al. Journal of Chemical Information and Modeling
- XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks
- (2013) Jed Zaretzki et al. Journal of Chemical Information and Modeling
- Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise
- (2013) David Ryan Koes et al. Journal of Chemical Information and Modeling
- Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
- (2013) Katja Hansen et al. Journal of Chemical Theory and Computation
- CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data
- (2013) Jing Huang et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- High-throughput sequencing for biology and medicine
- (2013) W. W. Soon et al. Molecular Systems Biology
- Evaluation of methods for modeling transcription factor sequence specificity
- (2013) Matthew T Weirauch et al. NATURE BIOTECHNOLOGY
- Machine learning of molecular electronic properties in chemical compound space
- (2013) Grégoire Montavon et al. NEW JOURNAL OF PHYSICS
- Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- Exploring Chemical Space for Drug Discovery Using the Chemical Universe Database
- (2012) Jean-Louis Reymond et al. ACS Chemical Neuroscience
- Deep architectures for protein contact map prediction
- (2012) Pietro Di Lena et al. BIOINFORMATICS
- Predicting protein residue–residue contacts using deep networks and boosting
- (2012) Jesse Eickholt et al. BIOINFORMATICS
- Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties
- (2012) Tu Le et al. CHEMICAL REVIEWS
- Are Protein Force Fields Getting Better? A Systematic Benchmark on 524 Diverse NMR Measurements
- (2012) Kyle A. Beauchamp et al. Journal of Chemical Theory and Computation
- Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking
- (2012) Michael M. Mysinger et al. JOURNAL OF MEDICINAL CHEMISTRY
- Protein structure prediction from sequence variation
- (2012) Debora S Marks et al. NATURE BIOTECHNOLOGY
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Predicting residue–residue contacts using random forest models
- (2011) Yunqi Li et al. BIOINFORMATICS
- A Kirkwood-Buff Derived Force Field for Aqueous Alkali Halides
- (2011) Moon Bae Gee et al. Journal of Chemical Theory and Computation
- SPINE X: Improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles
- (2011) Eshel Faraggi et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- PubChem's BioAssay Database
- (2011) Y. Wang et al. NUCLEIC ACIDS RESEARCH
- Evaluation of residue-residue contact predictions in CASP9
- (2011) Bohdan Monastyrskyy et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- How Fast-Folding Proteins Fold
- (2011) K. Lindorff-Larsen et al. SCIENCE
- Quantitative Nanostructure−Activity Relationship Modeling
- (2010) Denis Fourches et al. ACS Nano
- Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
- (2010) Geoffroy Hautier et al. CHEMISTRY OF MATERIALS
- NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations
- (2010) M. Valiev et al. COMPUTER PHYSICS COMMUNICATIONS
- 3D-QSAR in Drug Design - A Review
- (2010) Jitender Verma et al. CURRENT TOPICS IN MEDICINAL CHEMISTRY
- Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]
- (2010) I Arel et al. IEEE Computational Intelligence Magazine
- Extended-Connectivity Fingerprints
- (2010) David Rogers et al. Journal of Chemical Information and Modeling
- PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints
- (2010) Chun Wei Yap JOURNAL OF COMPUTATIONAL CHEMISTRY
- Quantitative Structure-Fluorescence Property Relationship Analysis of a Large BODIPY Library
- (2010) Andreas Schüller et al. Molecular Informatics
- Best Practices for QSAR Model Development, Validation, and Exploitation
- (2010) Alexander Tropsha Molecular Informatics
- Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue–residue contacts
- (2009) Patrik Björkholm et al. BIOINFORMATICS
- Human drug hepatotoxicity: a contemporary clinical perspective
- (2009) David N Assis et al. Expert Opinion on Drug Metabolism & Toxicology
- Influence Relevance Voting: An Accurate And Interpretable Virtual High Throughput Screening Method
- (2009) S. Joshua Swamidass et al. Journal of Chemical Information and Modeling
- Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data
- (2009) Sebastian G. Rohrer et al. Journal of Chemical Information and Modeling
- CHARMM: The biomolecular simulation program
- (2009) B. R. Brooks et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Simulation of Osmotic Pressure in Concentrated Aqueous Salt Solutions
- (2009) Yun Luo et al. Journal of Physical Chemistry Letters
- Predicting Continuous Local Structure and the Effect of Its Substitution for Secondary Structure in Fragment-Free Protein Structure Prediction
- (2009) Eshel Faraggi et al. STRUCTURE
- Support vector machines and its applications in chemistry
- (2008) Hongdong Li et al. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
- Progress and challenges in protein structure prediction
- (2008) Yang Zhang CURRENT OPINION IN STRUCTURAL BIOLOGY
- Prediction of Fungicidal Activities of Rice Blast Disease Based on Least-Squares Support Vector Machines and Project Pursuit Regression
- (2008) Hongying Du et al. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
- Mold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics
- (2008) Huixiao Hong et al. Journal of Chemical Information and Modeling
- Accelerating Density Functional Calculations with Graphics Processing Unit
- (2008) Koji Yasuda Journal of Chemical Theory and Computation
- GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation
- (2008) Berk Hess et al. Journal of Chemical Theory and Computation
- Consistent blind protein structure generation from NMR chemical shift data
- (2008) Y. Shen et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
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