Automatic Generation of Complementary Descriptors with Molecular Graph Networks

By C. Merkwirth, Thomas Lengauer. 2005

119 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Graph Learning, Computational Chemistry & Drug Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Jure Leskovec
2. William L. Hamilton
3. Sam Schoenholz
4. Christopher Morris
5. Matthias Fey
6. Rex Ying
7. Jan Eric Lenssen
8. Vijay S. Pande

In Molecular Biology

1. Jure Leskovec
2. Rex Ying
3. William L. Hamilton
4. Petar Velickovic
5. Jiaxuan You
6. Matthias Fey
7. Christopher Morris
8. Vijay S. Pande

In Graph Learning

1. Jure Leskovec
2. William L. Hamilton
3. Matthias Fey
4. Christopher Morris
5. Rex Ying
6. Petar Velickovic
7. Jiaxuan You
8. Tommi S. Jaakkola

In Computational Chemistry & Drug Discovery

1. Jure Leskovec
2. Sam Schoenholz
3. Rex Ying
4. Jiaxuan You
5. Vijay S. Pande
6. Patrick F. Riley
7. Steven M. Kearnes
8. Ola Engkvist

Citations in the corpus

• Neural Message Passing for Quantum Chemistry (2017, methodology): “While neural networks have been applied in a variety of situations (Merkwirth & Lengauer, 2005; Micheli, 2009; Lusci et al., 2013; Duvenaud et al., 2015), they have yet to become widely adopted.”
• Molecular graph convolutions: moving beyond fingerprints (2016, methodology): “In related work, a simple un-weighted sum (Duvenaud et al., 2015; Merkwirth and Lengauer, 2005; Lusci et al., 2013) or root-mean-square (Dieleman, March 17, 2015) (RMS) reduction is used.”
• Hierarchical Graph Representation Learning with Differentiable Pooling (2018, methodology): “GNNs have been applied to a wide variety of tasks, including node classification [16, 21], link prediction [31], graph classification [7, 11, 39], and chemoinformatics [14, 19, 27, 28, 32].”
• Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (2018, background): “…GraphSAGE (Hamilton, Ying, and Leskovec 2017a), SplineCNN (Fey et al. 2018), and the spectral approaches proposed in (Bruna et al. 2014; Defferrard, X., and Vandergheynst 2016; Kipf and Welling 2017)—all of which descend from early work in (Merkwirth and Lengauer 2005) and (Scarselli et al. 2009b).”
• TUDataset: A collection of benchmark datasets for learning with graphs (2020, background): “…Velickovic et al., 2018), and the spectral approaches proposed in, e.g., (Bruna et al., 2014; Defferrard et al., 2016; Kipf & Welling, 2017; Monti et al., 2017)—all of which descend from early work in (Kireev, 1995; Merkwirth & Lengauer, 2005; Sperduti & Starita, 1997; Scarselli et al., 2009).”
• Graph Representation Learning (2020, methodology): “We begin here with the most basic GNN framework, which is a simplification of the original GNN models proposed by Merkwirth and Lengauer [2005] and Scarselli et al.”
• The rise of deep learning in drug discovery. (2018, methodology): “This idea was first explored by Merkwirth et al . in 2005 [45].”
• Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (2019, background): “…Velickovic et al., 2018), and the spectral approaches proposed in, e.g., (Bruna et al., 2014; Defferrard et al., 2016; Kipf & Welling, 2017; Monti et al., 2017)—all of which descend from early work in (Kireev, 1995; Merkwirth & Lengauer, 2005; Sperduti & Starita, 1997; Scarselli et al., 2009).”
• Combinatorial optimization and reasoning with graph neural networks (2021, background): “, [16, 27, 68, 90]—all of which descend from early work in [69, 88, 120, 113].”