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Making Graph Neural Networks Worth It for Low-Data Molecular Machine
  Learning

Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning

24 November 2020
Aneesh S. Pappu
Brooks Paige
    GNNAI4CE
ArXiv (abs)PDFHTML

Papers citing "Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning"

6 / 6 papers shown
Title
Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for
  Molecular Graph Classification
Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classification
Jakub Adamczyk
Wojciech Czech
104
2
0
16 Jul 2024
Embracing assay heterogeneity with neural processes for markedly
  improved bioactivity predictions
Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions
Lucian Chan
M. Verdonk
C. Poelking
51
4
0
17 Aug 2023
Bringing Atomistic Deep Learning to Prime Time
Bringing Atomistic Deep Learning to Prime Time
Nathan C. Frey
S. Samsi
Bharath Ramsundar
Connor W. Coley
V. Gadepally
AI4CE
76
0
0
09 Dec 2021
Scalable Geometric Deep Learning on Molecular Graphs
Scalable Geometric Deep Learning on Molecular Graphs
Nathan C. Frey
S. Samsi
Joseph McDonald
Lin Li
Connor W. Coley
V. Gadepally
GNNAI4CE
60
4
0
06 Dec 2021
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave
  Functions
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
Nicholas Gao
Stephan Günnemann
86
40
0
11 Oct 2021
Molecular machine learning with conformer ensembles
Molecular machine learning with conformer ensembles
Simon Axelrod
Rafael Gómez-Bombarelli
AI4CE
65
51
0
15 Dec 2020
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