Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways.

By Masahiro Hattori, Yasushi Okuno, Susumu Goto, Minoru Kanehisa. Journal of the American Chemical Society, 2003

489 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/46b693f5995df608892905846c8a7d7fa9a71222

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In Molecular Biology

Citations in the corpus

Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework (2010, methodology): “We computed the chemical structure similarities between compounds using SIMCOMP (Hattori et al., 2003), a program that finds the common substructures between two compounds and outputs the global similarity score based on a graph alignment algorithm.”
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction (2016, methodology): “For a pair of compounds, the similarity between their chemical structures was measured by the SIMCOMP algorithm [34].”
The KEGG resource for deciphering the genome (2004, methodology): “A new feature implemented in the COMPOUND database is the KCF (KEGG Chemical Function) representation of chemical structures shown in Figure 1 and the resulting graph-based chemical structure comparison method (4).”
Toward more realistic drug–target interaction predictions (2014, unknown): “These data are available at: http://users.utu.fi/aatapa/data/Drug Target”
KEGG as a glycome informatics resource. (2006, methodology): “GLYCAN, which contains 11 066 unique entries (as of 1 July 2005), is a publicly accessible 5 glycan database at http://www.genome.jp/kegg/glycan/.”
The worst-case time complexity for generating all maximal cliques and computational experiments (2006, unknown): “, in clustering and in bioinformatics [15,16,8].”
ChemMine tools: an online service for analyzing and clustering small molecules (2011, background): “The underlying MCS algorithm often provides the most accurate and sensitive similarity measure, especially for compounds with large size differences (40,41).”
From genomics to chemical genomics: new developments in KEGG (2005, background): “This comparison is based on atom typing, which is the conversion of regular atomic (C, N, O, S, P and so on) representation to what we call KCF representation that consists of 68 atom types distinguishing functional groups 95 and atomic environments (7).”
Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization (2012, methodology): “Yamanishi et al. (2008) calculate the structural similarities between drug compounds using SIMCOMP (Hattori et al., 2003), which represents drug compounds as graphs and calculates a similarity score based on the size of the common substructures between two graphs.”
Drug-target interaction prediction by learning from local information and neighbors (2013, methodology): “The chemical structure similarities between drugs are computed with SIMCOMP (Hattori et al., 2003), which uses a graph alignment algorithm to get a global similarity score based on the size of the common substructures between two compounds.”
Deep learning with feature embedding for compound-protein interaction prediction (2016, methodology): “For instance, a drug-protein interaction proﬁle kernel [11] and a graph-based method SIMCOMP [15] were used to compare two drugs or compounds, and the normalized Smith-Waterman score [13] was typically applied to assess the closeness between two targets (proteins).”
Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, methodology): “Drug similarity was computed from the chemical structures of drugs (obtained from KEGG LIGAND [9]) by using SIMCOMP [61], which computes the drug similarity between two drugs d and d 0 as follows: where j d \ d 0 j is the number of all substructures shared by d and d 0 and j d [ d 0 j is the number…”
Supervised prediction of drug–target interactions using bipartite local models (2009, methodology): “We computed the chemical structure similarity between compounds using SIMCOMP (Hattori et al. , 2003) which provides a global similarity score based on the size of common substructures between compounds using a graph alignment algorithm.”