Martin Vögele
Stanford University
Atomic force microscopyBiophysicsVesicleDielectricDiffusion (business)IonDeposition (phase transition)Chemical physicsNanotechnologyPore-forming toxinAqueous solutionChemistryMaterials scienceTension (physics)Transmembrane proteinPneumolysinStyrenePerforation (oil well)SulfonateLayer (electronics)Hydrodynamic theoryLipid bilayerComputer scienceMembraneSubstrate (electronics)Chemical engineeringCarbon nanotubeMolecular dynamicsElectrolyte
22Publications
8H-index
237Citations
Publications 26
Newest
#2Martin VögeleH-Index: 8
Last. Risi KondorH-Index: 24
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#2Martin VögeleH-Index: 8
Last. Risi KondorH-Index: 24
view all 12 authors...
Source
#2Martin VögeleH-Index: 8
Last. Risi KondorH-Index: 24
view all 12 authors...
Source
#2Martin VögeleH-Index: 8
Last. Risi KondorH-Index: 24
view all 12 authors...
Source
#2Martin VögeleH-Index: 8
Last. Risi KondorH-Index: 24
view all 12 authors...
Source
#2Martin VögeleH-Index: 8
Last. Risi KondorH-Index: 24
view all 12 authors...
Source
#2Martin VögeleH-Index: 8
Last. Risi KondorH-Index: 24
view all 12 authors...
Source
#2Martin VögeleH-Index: 8
Last. Risi KondorH-Index: 24
view all 12 authors...
Source
#1Raphael J. L. Townshend (Stanford University)H-Index: 7
#2Martin Vögele (Stanford University)H-Index: 8
Last. Risi KondorH-Index: 24
view all 12 authors...
While a variety of methods have been developed for predicting molecular properties, deep learning networks that operate directly on three-dimensional molecular structure have recently demonstrated particular promise. In this work we present ATOM3D, a collection of both novel and existing datasets spanning several key classes of biomolecules, to systematically assess such learning methods. We develop three-dimensional molecular learning networks for each of these tasks, finding that they consiste...
While a variety of methods have been developed for predicting molecular properties, deep learning networks that operate directly on three-dimensional molecular structure have recently demonstrated particular promise. In this work we present ATOM3D, a collection of both novel and existing datasets spanning several key classes of biomolecules, to systematically assess such learning methods. We develop three-dimensional molecular learning networks for each of these tasks, finding that they consiste...
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