박사

Deep learning approaches to predictions of liquid properties

임현태 2020년
논문상세정보
' Deep learning approaches to predictions of liquid properties' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 화학과 응용과학
  • Deep learning
  • Liquid property
  • Liquid system
  • Solubility
  • Solvation free energy
  • Structure-property relationship
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
2,655 0

0.0%

' Deep learning approaches to predictions of liquid properties' 의 참고문헌

  • [94] Langer, R.; Tirrell, D. A. Designing materials for biology and medicine. Nature 2004, 428, 487–492.
    428 , 487–492 . [2004]
  • [92] Stupp, S. I. Self-Assembly and Biomaterials. Nano Letters 2010, 10, 4783–4786.
  • [89] Rothemund, P. W. K. Folding DNA to create nanoscale shapes and patterns. Nature 2006, 440, 297–302.
  • [85] Breiman, L. Random Forests. Machine Learning 2001, 45, 5–32.
  • [64] Chollet, F.; others, Keras; 2015.
  • [62] Marenich, A. V.; Kelly,C. P.; Thompson, J. D.; Hawkins, G. D.;Chambers,C.C.; Giesen, D. J.; Winget, P.;Cramer,C. J.; Truhlar, D. G. Minnesota Solvation Database – version 2012; 2012; Published: University of Minnesota, Minneapolis.
    D. G. Minnesota Solvation Database – version [2012]
  • [3] Klamt, A.; Schu¨urmann, G. COSMO: a new approach to dielectric ¨ screening in solvents with explicit expressions for the screening energy and its gradient. J. Chem. Soc., Perkin Trans. 2 1993, 799–805.
  • [149] Tuckerman, M. E. Statistical mechanics: theory and molecular simulation; Oxford University Press, 2010.
  • [146] Whitelam, S. Large deviations in the presence of cooperativity and slow dynamics. Physical Review E 2018, 97, 062109.
  • [124] Shirts, M. R.; Chodera, J. D. Statistically optimal analysis of samples from multiple equilibrium states. The Journal of Chemical Physics 2008, 129, 124105.
  • [123] Bennett, C. H. Efficient estimation of free energy differences from Monte Carlo data. Journal of Computational Physics 1976, 22, 245– 268.
  • [11] Mennucci, B. Polarizable continuum model: Polarizable continuum 108model. Wiley Interdisciplinary Reviews: Computational Molecular Science 2012, 2, 386–404.
  • [119] Gaspard, P. Time-Reversed Dynamical Entropy and Irreversibility in Markovian Random Processes. Journal of Statistical Physics 2004, 117, 599–615.
  • [115] Touchette, H. The large deviation approach to statistical mechanics. Physics Reports 2009, 478, 1–69.
  • [110] Chetrite, R.; Touchette, H. Nonequilibrium Microcanonical and Canonical Ensembles and Their Equivalence. Physical Review Letters 2013, 111, 120601.
  • [108] Garrahan, J. P. Classical stochastic dynamics and continuous matrix product states: gauge transformations, conditioned and driven processes, and equivalence of trajectory ensembles. Journal of Statistical Mechanics: Theory and Experiment 2016, 2016, 073208.
  • [105] Rapaport, D. C. Role of Reversibility in Viral Capsid Growth: A Paradigm for Self-Assembly. Physical Review Letters 2008, 101, 186101.
  • Y. Neural Machine Translation by Jointly Learning to Align and Translate .
    [2016]
  • Y. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
    [2014]
  • Y. Delfos : deep learning model for prediction of sol110vation free energies in generic organic solvents
    10 , 8306–8315 . [2019]
  • Y. ; Lee , D. ; Park , C. B. ; Kang ,
    Motif for Reversible Electrochemical
  • X. S. Single-Molecule MichaelisMenten Equations .
    109 , 19068–19081 . [2005]
  • X. S. Nonequilibrium Steady State of a Nanometric Biochemical System : Determining the Thermodynamic Driving Force from Single Enzyme Turnover Time Traces
    5 , 2373–2378 . [2005]
  • X. S. Ever-fluctuating single enzyme molecules : Michaelis-Menten equation revisited
    2 , 87–94 . [2006]
  • W. Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field
    6 , 1509–1519 . [2010]
  • W. E. Enthalpies of solution and enthalpies of solvation of organic solutes in ethylene glycol at 298.15 K : Prediction and analysis of intermolecular interaction contributions
    648 , 91–99 . [2017]
  • W. COSMO-RS : An Alternative to Simulation for Calculating Thermodynamic Properties of Liquid Mixtures
    1 , 101–122 . [2010]
  • V. S. Emergence of Glass-like Behavior in Markov State Models of Protein Folding Dynamics
    135 , 5501–5504 . [2013]
  • V. MoleculeNet : a benchmark for molecular machine learning
    9 , 513–530 . [2018]
  • U. Assigning numbers to the arrows : Parameterizing a gene regulation network by using accurate expression kinetics .
    99 , 10555–10560 . [2002]
  • The fluctuation theorem and Green–Kubo relations
    112 , 9727–9735 [2000]
  • The charge-asymmetric nonlocally determined local-electric ( CANDLE ) solvation model
    142 , 064107 . [2015]
  • The COSMO and COSMO-RS solvation models : COSMO and COSMO-RS
    8 , e1338 . [2018]
  • TensorFlow : Large-Scale Machine Learning on Heterogeneous Systems
    [2015]
  • T. Solvents and Solvent Effects in Organic Chemistry : REICHARDT : SOLV.EFF . 4ED O-BK
    [2010]
  • T. Electron-deficient anthraquinone derivatives as cathodic material for lithium ion batteries
    328 , 228–234 [2016]
  • Scikit-learn : Machine Learning in Python
    12 , 2825–2830 [2011]
  • S. Solvation free energies and partitionCoefficients with theCoarse-grained and hybrid all-atom/coarse-grained MARTINI models
    31 , 867–876 . [2017]
  • S. Similarity of ensembles of trajectories of reversible and irreversible growth processes
    96 , 042126 . [2017]
  • S. S. Application of DFTbased machine learning for developing molecular electrode materials in Li-ion batteries
    8 , 39414–39420 . [2018]
  • S. Rare behavior of growth processes via umbrella sampling of trajectories .
    97 , 032123 . [2018]
  • S. Mol2vec : Unsupervised Machine Learning Approach withChemical Intuition
    58 , 27–35 [2018]
  • S. M. Force production by single kinesin motors
    2 , 718–723 . [2000]
  • S. Large-scaleComparison of machine learning methods for drug target prediction onChEMBL
    9 , 5441–5451 . [2018]
  • S. Discovering a TransferableCharge Assignment Model Using Machine Learning
    9 , 4495–4501 . [2018]
  • S. Biological Implications of Dynamical Phases in Non-equilibrium Networks
    162 , 1183–1202 [2016]
  • S. Atomic decomposition of the protein solvation free energy and its application to amyloid-beta protein in water
    135 , 034506 . [2011]
  • S. Analyzing mechanisms and microscopic reversibility of self-assembly .
    135 , 214505 . [2011]
  • Role of substrate unbinding in Michaelis-Menten enzymatic reactions
    111 , 4391–4396 . [2014]
  • R. Variance-corrected Michaelis-Menten equation predicts transient rates of single-enzyme reactions and re127sponse times in bacterial gene-regulation
    5 , 17820 . [2015]
  • R. S. The Original MichaelisConstant : Translation of the 1913 Michaelis–Menten Paper
    50 , 8264–8269 . [2011]
  • R. Quantum MechanicalContinuum Solvation Models
    105 , 2999–3094 [2005]
  • R. L.ControllingCrystal self-assembly using a realtime feedback scheme .
    138 , 094502 . [2013]
  • R. L. The Statistical Mechanics of Dynamic Path122ways to Self-Assembly .
    66 , 143–163 . [2015]
  • R. L. Quantifying reversibility in a phase-separating lattice gas : An analogy with self-assembly .
    85 , 021112 [2012]
  • R. L. Predicting the self-assembly of a modelColloidalCrystal
    7 , 6294 . [2011]
  • QSAR Modeling : Where Have You Been ? Where Are You Going To ?
    57 , 4977– 5010 [2014]
  • P. Molec112ular graph convolutions : moving beyond fingerprints
    30 , 595–608 . [2016]
  • P. Learning long-term dependencies with gradient descent is difficult .
    5 , 157–166 . [1994]
  • P. Large Deviations and Ensembles of Trajectories in Stochastic Models
    184 , 304–317 . [2010]
  • P. L. Transition path sampling : throwing ropes over rough mountain passes , in the dark .
    53 , 291–318 . [2002]
  • P. L. The role of collective motion in examples of coarsening and self-assembly
    5 , 1251–1262 . [2009]
  • P. L. Dynamic phase transitions in simple driven kinetic networks .
    89 , 062108 . [2014]
  • P. L. Avoiding unphysical kinetic traps in Monte Carlo simulations of strongly attractive particles
    127 , 154101 [2007]
  • P. Deep Architectures and Deep Learning in Chemoinformatics : The Prediction of Aqueous Solubility for Drug-Like Molecules
    53 , 1563–1575 . [2013]
  • One hundred years of Michaelis–Menten kinetics
    4 , 3–9 . [2015]
  • OPLS3 : A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins
    12 , 281–296 [2016]
  • Novel Chemical Kinetics for a Single Enzyme Reaction : Relationship between Substrate Concentration and the Second Moment of Enzyme Reaction Time
    114 , 9840–9847 . [2010]
  • N. Self-consistent continuum solvation ( SCCS ) : The case of charged systems
    139 , 214110 . [2013]
  • Machine learning for molecular and materials science
    559 , 547–555 . [2018]
  • M. Semi-Supervised Classification with Graph Convolutional Networks
    arXiv:1609.02907 [ cs , stat [2017]
  • M. R. K. Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics
    10 , e0141287 . [2015]
  • M. Michaelis-Menten reaction scheme as a unified approach towards the optimal restart problem
    92 , 060101 . [2015]
  • M. L. Die Kinetik der Invertinwirkung
    49 , 333 . [1913]
  • M. J. Anisotropy of building blocks and their assembly into complex structures
    6 , 557–562 . [2007]
  • M. H. Abraham model correlations for describing the thermodynamic properties of solute transfer into pentyl acetate based on headspace chromatographic and solubility measurements
    124 , 133– 140 [2018]
  • M. F. Using Markov state models to study self-assembly
    140 , 214101 . [2014]
  • M. Extended-Connectivity Fingerprints
    50 , 742–754 . [2010]
  • M. Calculation of Solvation Free Energies with DCOSMO-RS
    119 , 5439–5445 . [2015]
  • M. Beyond molecules : Self-assembly of mesoscopic and macroscopic components
    99 , 4769–4774 . [2002]
  • Long short-term memory
    9 , 1735–1780 . [1997]
  • Learning continu- ´ ous and data-driven molecular descriptors by translating equivalent chemical representations .
    10 , 1692–1701 . [2019]
  • L. M. The Chemical Master Equation Approach to Nonequilibrium Steady-State of Open Biochemical Systems : Linear Single-Molecule Enzyme Kinetics and Nonlinear Biochemical Re128action Networks
    11 , 3472–3500 . [2010]
  • L. Deep contextualized word representations . arXiv:1802.05365 2018
  • K.-R. SchNetPack : A Deep Learning ¨ Toolbox For Atomistic Systems .
    15 , 448–455 . [2019]
  • K.-R. SchNet – A deep learning architecture for molecules ¨ and materials
    148 , 241722 . [2018]
  • K. R. ; ¨ Tkatchenko , A. Quantum-chemical insights from deep tensor neural networks
    8 , 13890 . [2017]
  • K. High-efficiency and high-power rechargeable lithium–sulfur dioxide batteries exploiting conventional carbonate-based electrolytes .
    8 , 14989 . [2017]
  • K. F. Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
    57 , 1757–1772 . [2017]
  • K. Bidirectional recurrent neural networks .
    45 , 2673–2681 . [1997]
  • J. W. Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field
    37 , 2749– 2758 . [2016]
  • J. S. ESOL : Estimating Aqueous Solubility Directly from Molecular Structure
    44 , 1000–1005 [2004]
  • J. Quantitative Interpretation of the Randomness in Single Enzyme Turnover Times
    101 , 519–524 . [2011]
  • J. P. FreeSolv : a database of experimental and calculated hydration free energies , with input files
    28 , 711–720 . [2014]
  • J. P. Fluctuating observation time ensembles in the thermodynamics of trajectories
  • J. P. Finitetemperature critical point of a glass transition
    107 , 12793–12798 . [2010]
  • J. Nonclassical Kinetics of Clonal yet Heterogeneous Enzymes .
    8 , 3152–3158 . [2017]
  • J. Neural network based language models for highly inflective languages .
    pp 4725–4728 [2009]
  • J. J. ZINC 15 – Ligand Discovery for Every120one .
    55 , 2324– 2337 [2015]
  • J. Identifying Structure–Property Relationships through SMILES Syntax Analysis with Self-Attention Mechanism
    59 , 914–923 . [2019]
  • J. H. Improving solvation energy predictions using the SMD solvation method and semiempirical electronic structure methods
    149 , 104102 . [2018]
  • J. H. Greedy Function Approximation : A Gradient Boosting Machine
    29 , 1189–1232 . [2001]
  • J. D. Self-Assembly at a Nonequilibrium Critical Point
    112 , 155504 . [2014]
  • H. L. The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service
    5 , 107–113 [1965]
  • H. A modern solvation theory : quantum chemistry and statistical chemistry
    15 , 7450 . [2013]
  • H. ; Lazzaro , L. ; Dahlgren , B. ; Sjorgen
    [2014]
  • Generic Schemes for Single-Molecule Kinetics . 1 : Self-Consistent Pathway Solutions for Renewal Processes
    112 , 12867–12880 . [2008]
  • G. Molecular fingerprint similarity search in vir- ´ tual screening .
    71 , 58–63 . [2015]
  • G. H. The DIPPR databases
    17 , 223–232 . [1996]
  • G. E. Neural Message Passing for Quantum Chemistry
    [2017]
  • F. Thermodynamic Formalism for Systems with Markov Dynamics
    127 , 51–106 . [2007]
  • F. First-order dynamical phase transition in models of glasses : an approach based on ensembles of histories .
    42 , 075007 . [2009]
  • F. Dynamical First-Order Phase Transition in Kinetically Constrained Models of Glasses
    98 , 195702 . [2007]
  • Efficient Estimation of Word Representations in Vector Space
    [2013]
  • E. Single-molecule enzymology : stochastic Michaelis–Menten kinetics
    101-102 , 565–576 . [2002]
  • Distributed Representations of Words and Phrases and their Compositionality .
    [2013]
  • Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network
  • Deep reinforcement learning for de novo drug design
    4 , eaap7885 . [2018]
  • Deep learning in neural networks : An overview
    61 , 85–117 . [2015]
  • Decision trees : a recent overview
    39 , 261–283 . [2013]
  • D. Preparation and Relaxation of Very Stable Glassy States of a Simulated Liquid
    107 , 275702 . [2011]
  • D. L. Approaches for Calculating Solvation Free Energies and Enthalpies Demonstrated with an Update of the FreeSolv Database
    62 , 1559–1569 . [2017]
  • D. J. Hierarchical self-assembly of chiral fibres from achiral particles
    2 , 651–657 . [2012]
  • D. G. Universal Solvation Model Based on Solute Electron Density and on a Continuum Model of the Solvent Defined by the Bulk Dielectric Constant and Atomic Surface Tensions
    113 , 6378–6396 [2009]
  • D. G. Self-Consistent Reaction Field Model for Aqueous and Nonaqueous Solutions Based on Accurate Polarized Partial Charges
    3 , 2011–2033 . [2007]
  • D. G. Generalized Born Solvation Model SM12
    9 , 609–620 . [2013]
  • D. G. A Universal Approach to Solvation Modeling
    41 , 760–768 . [2008]
  • D. Force Field Benchmark of Organic Liquids . 2 . Gibbs Energy of Solvation
    55 , 1192–1201 [2015]
  • D. Fluctuation-dissipation ratios in the dynamics of self-assembly
    76 , 021119 . [2007]
  • D. Dynamic Order-Disorder in Atomistic Models of Structural Glass Formers
    323 , 1309–1313 . [2009]
  • D. Constrained dynamics of localized excitations causes a non-equilibrium phase transition in an atomistic model of glass formers
    136 , 184509 . [2012]
  • Calculations of Solvation Free Energy through Energy Reweighting from Molecular Mechanics to Quantum Mechanics
    12 , 499– 511 [2016]
  • C. W. Handbook of Stochastic Methods : For Physics
    [1985]
  • C. S. Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs
    21 , 13706–13720 . [2019]
  • C. P. First-Order Phase Transition in a Model Glass Former : Coupling of Local Structure and Dynamics
    109 , 195703 . [2012]
  • C. Molecular self-assembly and nanochemistry : a chemical strategy for the synthesis of nanostructures .
    254 , 1312–1319 . [1991]
  • C. L. Kinesin Moves by an Asymmetric Hand-Over-Hand Mechanism
    302 , 2130–2134 . [2003]
  • C. Glove : Global Vectors for Word Representation . Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ( EMNLP )
    pp 1532–1543 [2014]
  • C. D. Effective Approaches to Attention-based Neural Machine Translation
  • C. Catalytic Gold Nanoparticles for Nanoplasmonic Detection of DNA Hybridization
    50 , 11994–11998 . [2011]
  • Best Practices for QSAR Model Development , Validation , and Exploitation
    29 , 476–488 . [2010]
  • B. R. Predicting hydra- ¨ tion free energies with a hybrid QM/MM approach : an evaluation of implicit and explicit solvation models in SAMPL4
    28 , 245–257 . [2014]
  • Attend and Tell : Neural Image Caption Generation with Visual Attention
    [2016]
  • A. SMILES2Vec : An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
  • A. M. Single-Molecule Kinetics of Exonuclease Reveal Base Dependence and Dynamic Disorder
    301 , 1235– 1238 . [2003]
  • A. E. ANI-1 : an extensible neural network potential with DFT accuracy at force field computational cost .
    8 , 3192–3203 . [2017]
  • A. Conductor-like Screening Model for Real Solvents : A New Approach to the Quantitative Calculation of Solvation Phenomena
    99 , 2224–2235 [1995]
  • A review of methods for the calculation of solution free energies and the modelling of systems in solution
    17 , 6174–6191 . [2015]
  • A new algorithm for Monte Carlo simulation of Ising spin systems
    17 , 10–18 . [1975]
  • A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification .
    10 , 8438–8446 . [2019]