Benchmark Data Set for in Silico Prediction of Ames Mutagenicity

352 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition (2018, neutral): “However, the best reported results on the ESOL data set were so far obtained by two molecular graph convolution 9,37 39 on this data set 26 but the former two achieved higher sensitivity values (Table 2). existing literature results (Table 3, Table S6).”
IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics (2017, background): “Among the 1742 Indian medicinal plants in our database, Ginkgo biloba (136), Panax ginseng (135) and Allium sativum (86) have the largest number of documented therapeutic uses.”
jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints (2011, methodology): “The ECFP and LSTAR encodings achieved the best results, comparable to the results of several supervised classifiers presented by Hansen et al. [15] using dragonX [34] descriptors.”
Uniquely Discriminating Molecular Structures Using Novel Eigenvalue-Based Descriptors (2012, methodology): “AG3981 The database has been generated from a freely available database called Ames Genetoxicity [13, 27].”
ProTox-II: a webserver for the prediction of toxicity of chemicals (2018, methodology): “ProTox-II, mutagenicity prediction is based on the benchmark data set from Ames test (22) as well as CEBS database (5).”