Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity.

By A. K. Debnath, R. L. Compadre, Gargi Debnath, A. Shusterman, C. Hansch. 1991

Subfields: Computational Chemistry & Drug Discovery

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Citations in the corpus

Discriminative Embeddings of Latent Variable Models for Structured Data (2016, neutral): “MUTAG (Debnath et al., 1991), NCI1 and NCI109 (Wale et al.”
Pre-training Graph Neural Networks with Kernels (2018, neutral): “We report experimental results on three datasets containing biological node-labeled graphs, namely MUTAG [Debnath et al. , 1991], PTC [Toivonen et al. , 2003], NCI1 [Wale et al. , 2008], Each graph represents a chemical compound, where nodes are labeled with the atom type, and edges represent bonds…”
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (2017, neutral): “iction accuracy. NCI1 and NCI109 [39] consist of graph representations of chemical compounds screened for activity against nonsmall cell lung cancer and ovarian cancer cell lines, respectively. MUTAG [10] is a dataset of nitro compounds labeled according to whether or not they have a mutagenic effec”
A Comprehensive Survey on Graph Neural Networks (2019, neutral): “logical Graphs PPI [109] 24 56944 818716 50 121 [15], [19], [24], [27], [28] [46], [48], [62] NCI-1 [110] 4100 - - 37 2 [26], [44], [47], [52], [57] NCI-109 [110] 4127 - - 38 2 [26], [44], [52] MUTAG [111] 188 - - 7 2 [26], [44], [52] D&D [112] 1178 - - - 2 [26], [47], [52] QM9 [113] 133885 - - ”
Propagation kernels: efficient graph kernels from propagated information (2014, background): “mutag contains 188 sets of mutagenic aromatic and heteroaromatic nitro compounds, and the label refers to their mutagenic effect on the Gramnegative bacterium Salmonella typhimurium (Debnath et al. 1991).”
The Multiscale Laplacian Graph Kernel (2016, methodology): “Method MUTAG[22] PTC[23] ENZYMES[2] PROTEINS[2] NCI1[24] NCI109[24] WL 84.”
Graph kernels for chemical informatics (2005, methodology): “Among the 230 compounds, however, only 188 (125 positive, 63 negative) are considered to be learnable [Debnath et al., 1991] and thus are used in our simulations (Table 3).”
Multiple Instance Regression (2001, background): “Second, multiple regression appears well-suited for the particular task of drug activity prediction(Hansch et al., 1962; Debnath et al., 1991) that was the original motivation for multiple instance learning.”
Identity-aware Graph Neural Networks (2021, methodology): “For graph-level comparison, we include experimental results over 3 datasets: MUTAG with 182 graphs (Debnath et al. 1991), PTC with 344 graphs (Helma and Kramer 2003), and PROTEINS .”
Fast subtree kernels on graphs (2009, background): “On MUTAG, it reaches the third best accuracy among all methods considered.”