scikit-fingerprints/MoleculeNet_ToxCast
MoleculeNet ToxCast ToxCast dataset [1], part of MoleculeNet [2] benchmark. It is intended to be used through scikit-fingerprints library. The task is to predict 617 toxicity targets from a large library of compounds based on in vitro high-throughput screening. All tasks are binary. Note that targets have missing values. Algorithms should be evaluated only on present labels. For training data, you may want to impute them, e.g. with zeros. Characteristic Description… See the full description on the dataset page: https://huggingface.co/datasets/scikit-fingerprints/MoleculeNet_ToxCast.
MoleculeNet ToxCast
ToxCast dataset [[1]](#1), part of MoleculeNet [[2]](#2) benchmark. It is intended to be used through scikit-fingerprints library.
The task is to predict 617 toxicity targets from a large library of compounds based on in vitro high-throughput screening. All tasks are binary.
Note that targets have missing values. Algorithms should be evaluated only on present labels. For training data, you may want to impute them, e.g. with zeros.
References
<a id="1">[1]</a> Ann M. Richard et al. "ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology" Chem. Res. Toxicol. 2016, 29, 8, 1225–1251 https://pubs.acs.org/doi/10.1021/acs.chemrestox.6b00135>
<a id="2">[2]</a> Wu, Zhenqin, et al. "MoleculeNet: a benchmark for molecular machine learning." Chemical Science 9.2 (2018): 513-530 https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc02664a