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convolutional networks on graphs for learning molecular fingerprints
Convolutional Networks On Graphs For Learning Molecular Fingerprints, supplied by Curran Associates Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
Convolutional Networks On Graphs For Learning Molecular Fingerprints, supplied by Curran Associates Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/convolutional+networks+on+graphs+for+learning+molecular+fingerprints/convolutional+networks+on+graphs+for+learning+molecular+fingerprints/pm38990780-349-19-36
Average 90 stars, based on 1 article reviews
convolutional networks on graphs for learning molecular fingerprints - by Bioz Stars,
2026-09
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other:Article Title: Constant size descriptors for accurate machine learning models of molecular properties. Article Snippet: Constant size descriptors for accurate machine learning models of molecular properties Christopher R. Collins, Geoffrey J. Gordon, O. Anatole von Lilienfeld, and David J. Yaron Citation: The Journal of Chemical Physics 148, 241718 (2018); doi: 10.1063/1.5020441 View online: https://doi.org/10.1063/1.5020441 View Table of Contents: http://aip.scitation.org/toc/jcp/148/24 Published by the American Institute of Physics Articles you may be interested in Alchemical and structural distribution based representation for universal quantum machine learning The Journal of Chemical Physics 148, 241717 (2018); 10.1063/1.5020710 Hierarchical modeling of molecular energies using a deep neural network The Journal of Chemical Physics 148, 241715 (2018); 10.1063/1.5011181 Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy The Journal of Chemical Physics 148, 241702 (2018); 10.1063/1.5003074 A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information The Journal of Chemical Physics 148, 241708 (2018); 10.1063/1.5017898 wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials The Journal of Chemical Physics 148, 241709 (2018); 10.1063/1.5019667 Maximally resolved anharmonic OH vibrational spectrum of the water/ZnO(10 0) interface from a highdimensional neural network potential The Journal of Chemical Physics 148, 241720 (2018); 10.1063/1.5012980 THE JOURNAL OF CHEMICAL PHYSICS 148, 241718 (2018) Constant size descriptors for accurate machine learning models of molecular properties Christopher R. Collins,1 Geoffrey J. Gordon,2 O. Anatole von Lilienfeld,3 and David J. Yaron1,a) 1Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA 2Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA 3Department of Chemistry, Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), University of Basel, 4056 Basel, Switzerland (Received 22 December 2017; accepted 8 March 2018; published online 27 March 2018) Two different classes of molecular representations for use in machine learning of thermodynamic and electronic properties are studied.. The representations are evaluated by monitoring the performance of linear and kernel ridge regression models on well-studied data sets of small organic molecules.. One class of representations studied here counts the occurrence of bonding patterns in the molecule. Article Title: Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor. Article Snippet: |