The Predictive Toxicology Challenge 2000-2001

By C. Helma, Ross D. King, Stefan Kramer, A. Srinivasan. 2001

270 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Text Categorization as a Graph Classification Problem (2015, background): “We believe graphs can help solve these issues like they did for instance with chemical compounds where repeating substructure patterns are good indicators of belonging to one particular class, e. g., predicting carcinogenicity in molecules (Helma et al., 2001).”
Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings (2015, unknown): “Weclassify graphs from four diﬀerent datasets, commonly used as benchmarks for graph kernels, ptc , mutag , enzyme and nci 1 s , see Table 2a for details. ptc is a set of graphs representing chemical compounds labeled according to their carcinogenic eﬀects on rats and mice [20].”
A Gentle Introduction to Deep Learning for Graphs (2019, background): “In terms of benchmarks for graph classification, there is a consistent number of chemical datasets used to evaluate performances of DGNs. 1The code is available at https://github.com/diningphil/PyDGN Among them, we mention NCI1 [125], PROTEINS, [17] D&D [31], MUTAG [28], PTC [60] and ENZYMES [106].”
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (2019, unknown): “[53] C.”
Graph Self-Supervised Learning: A Survey (2021, background): “The PTC [141] and PTCMR [142] datasets contain a series of chemical compounds, while the labels indicate whether they are carcinogenic for male and female rats.”
TUDataset: A collection of benchmark datasets for learning with graphs (2020, methodology): “Our collection contains small datasets commonly used in the early graph kernel literature such as M U - TAG (Debnath et al., 1991) and P TC (Helma et al., 2001), medium-sized datasets, e.g., N CI 1 and N CI 109 (Wale et al., 2008; Shervashidze et al., 2011), as well as several large datasets…”
Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures (2018, methodology): “The predictive toxicology challenge dataset (PTC) (Helma, King, Kramer, & Srinivasan, 2001) is composed of four datasets about molecules, labeled by their toxicity for female rats (fr) and mice (fm) and male rats (mr) and mice (mm).”
Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity (2005, background): “The Predictive Toxicology Challenge (PTC) dataset (Helma et al., 2001) reports the carcinogenicity of several hundred chemical compounds for male mice (MM), female mice (FM), male rats (MR) and female rats (FR).”
Graph kernels for chemical informatics (2005, background): “The Predictive Toxicology Challenge (PTC) dataset [Helma et al., 2001] reports the carcinogenicity of several hundred chemical compounds for Male Mice (MM), Female Mice (FM), Male Rats (MR) and Female Rats (FR) (Table 1).”
Optimal assignment kernels for attributed molecular graphs (2005, methodology): “We used the PTC dataset (Helma et al., 2001), which is the result of the following pharmaceutical experiments: Each of 417 chemical compounds is given to four types of test animals – Male Mouse (MM), Female Mouse (FM), Male Rat (MR) and Female Rat (FR).”