BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules
出版年份 2019 全文链接
标题
BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules
作者
关键词
-
出版物
JOURNAL OF COMPUTATIONAL CHEMISTRY
Volume 41, Issue 8, Pages 790-799
出版商
Wiley
发表日期
2019-12-17
DOI
10.1002/jcc.26128
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions
- (2019) Sandra Romero-Molina et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
- (2019) Oliver T. Unke et al. Journal of Chemical Theory and Computation
- Deep Learning in Chemistry
- (2019) Adam C. Mater et al. Journal of Chemical Information and Modeling
- MLatom : A program package for quantum chemical research assisted by machine learning
- (2019) Pavlo O. Dral JOURNAL OF COMPUTATIONAL CHEMISTRY
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- Representing molecular and materials data for unsupervised machine learning
- (2018) E. Swann et al. MOLECULAR SIMULATION
- MoleculeNet: a benchmark for molecular machine learning
- (2018) Zhenqin Wu et al. Chemical Science
- Machine learning for molecular and materials science
- (2018) Keith T. Butler et al. NATURE
- Planning chemical syntheses with deep neural networks and symbolic AI
- (2018) Marwin H. S. Segler et al. NATURE
- Molecular Dynamics Simulation for All
- (2018) Scott A. Hollingsworth et al. NEURON
- Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks
- (2018) Jack Hanson et al. BIOINFORMATICS
- Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
- (2017) Felix A. Faber et al. Journal of Chemical Theory and Computation
- Deep learning for computational chemistry
- (2017) Garrett B. Goh et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Protein secondary structure prediction: A survey of the state of the art
- (2017) Qian Jiang et al. JOURNAL OF MOLECULAR GRAPHICS & MODELLING
- Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
- (2017) Kun Yao et al. Journal of Physical Chemistry Letters
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- Quantum-chemical insights from deep tensor neural networks
- (2017) Kristof T. Schütt et al. Nature Communications
- Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
- (2017) Marwin H. S. Segler et al. ACS Central Science
- ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
- (2017) Justin S. Smith et al. Scientific Data
- An Empirical Polarizable Force Field Based on the Classical Drude Oscillator Model: Development History and Recent Applications
- (2016) Justin A. Lemkul et al. CHEMICAL REVIEWS
- Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
- (2016) Bing Huang et al. JOURNAL OF CHEMICAL PHYSICS
- Machine-learning-assisted materials discovery using failed experiments
- (2016) Paul Raccuglia et al. NATURE
- A general-purpose machine learning framework for predicting properties of inorganic materials
- (2016) Logan Ward et al. npj Computational Materials
- Constructing high-dimensional neural network potentials: A tutorial review
- (2015) Jörg Behler INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
- (2015) Katja Hansen et al. Journal of Physical Chemistry Letters
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Molecular Mechanics
- (2014) Kenno Vanommeslaeghe et al. CURRENT PHARMACEUTICAL DESIGN
- 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
- Quantum chemistry structures and properties of 134 kilo molecules
- (2014) Raghunathan Ramakrishnan et al. Scientific Data
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone ϕ, ψ and Side-Chain χ1 and χ2 Dihedral Angles
- (2012) Robert B. Best et al. Journal of Chemical Theory and Computation
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Systematic optimization of long-range corrected hybrid density functionals
- (2008) Jeng-Da Chai et al. JOURNAL OF CHEMICAL PHYSICS
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started