Graph kernels for chemical informatics

By Liva Ralaivola, S. Joshua Swamidass, Hiroto Saigo, Pierre Baldi. 2005

513 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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

In Materials Chemistry

In Molecular Biology

Citations in the corpus

Interpreting linear support vector machine models with heat map molecule coloring (2011, background): “Common molecular encodings that generate interpretable features are radial atom environments [3], depth first search paths [ 15 ], or extended connectivity fingerprints (ECFP) [16].”
Protein-ligand interaction prediction: an improved chemogenomics approach (2008, background): “While any of these kernels can theoretically be used as a target kernel in (3), we investigate in this article a restricted list of specific kernels described below, aimed at illustrating the flexibility of our framework and test various hypothesis.”
Efficient graphlet kernels for large graph comparison (2009, background): “Several studies have recently shown that these graph kernels can achieve state-of-the-art results on problems from bioinformatics and on benchmark datasets from chemistry [7, 8].”
Input Output Kernel Regression (2016, methodology): “The Tanimoto kernel (Ralaivola et al., 2005) is used for the scalar input kernel: .”
gBoost: a mathematical programming approach to graph classification and regression (2009, background): “Several kernels have been proposed so far (Kashima et al. 2003; Gärtner et al. 2003; Ralaivola et al. 2005; Fröhrich et al. 2006; Mahé et al. 2005; Mahé et al. 2006; Horváth et al. 2004).”
jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints (2011, methodology): “assume an average branching factor a, which is slightly above 1 for organic compounds [ 6 ], the depth-first search has a complexity of O(na d ). The average branch-”
Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures (2018, methodology): “In this section, we describe experiments performed on 78 datasets about organic molecules: the Mutagenesis dataset (Lodhi & Muggleton, 2005), four datasets from the predictive toxicology challenge, and 73 NCI datasets (Ralaivola, Swamidass, Saigo, & Baldi, 2005).”