Derivation and validation of toxicophores for mutagenicity prediction.

By J. Kazius, R. McGuire, R. Bursi. 2005

675 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Environmental Chemistry, Materials Chemistry

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Citations in the corpus

Interpreting linear support vector machine models with heat map molecule coloring (2011, methodology): “Second, we used the mutagenicity data set composed by Kazius et al. [10].”
How to Explain Individual Classification Decisions (2009, background): “The features shown in Figure 8 are known to detoxify certain toxicophores (again see Kazius, McGuire, and Bursi, 2005).”
DeepTox: Toxicity Prediction using Deep Learning (2016, background): “The constructed features can indicate functional groups or toxicophores (Kazius et al., 2005) as visualized in Figure 3.”
RetGK: Graph Kernels based on Return Probabilities of Random Walks (2018, background): “(iii) Graphs with continuous attributes: FRANK is a chemical molecule dataset [15].”
A survey on graph kernels (2019, neutral): “77 + + – – [119, 123] MSRC-9 221 8 40.”
In silico toxicity prediction by support vector machine and SMILES representation-based string kernel (2012, background): “To date, a large number of in silico toxicity models have been developed and increasing numbers of associated papers have been published [11–16].”
Weisfeiler and Lehman Go Cellular: CW Networks (2021, methodology): “molecules – MUTAG [45, 61], PTC, NCI1 and NCI109 [70]) to social networks (IMDB-B, IMDB-M, RDT-B).”
Discriminative prototype selection methods for graph embedding (2013, methodology): “Similar to the AIDS dataset, the Mutagenicity dataset follows the same approach of converting molecular compounds into attributed graphs [47].”
Graph Invariant Kernels (2015, background): “BURSI is made by 4337 molecules with mutagenicity (AMES) classification, with 2401 mutagens and 1936 nonmutagens [Kazius et al., 2005].”
Virtual screening strategies in drug discovery: a critical review. (2013, background): “For example, when preparing a collection for VS, it is appropriate to remove molecules that contain chemically reactive groups or other undesirable functionalities [17-23] that interfere with the screening detection techniques and cause them to elicit a positive signal.”
Toxicity Prediction using Deep Learning (2015, background): “We also calculated the similarity of each compound to 2 500 known toxicophore features, ie., patterns of substructures that were previously reported as toxicophores in the literature (Kazius et al., 2005).”
Graph Convolutional Networks with EigenPooling (2019, methodology): “ely used benchmark data sets for graph classification [21], which includes three protein graph data sets, i.e., ENZYMES[3, 36], PROTEINS[3, 10], and D&D[10, 37]; one mutagen data set Mutagenicity [20, 31] (We denoted as MUTAG in Table 1 and Table 2); and two data sets that consist of chemical co”