Research data supporting "Neural network activation similarity: A new measure to assist decision making in chemical toxicology"
Repository URI
Repository DOI
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Type
Dataset
Change log
Authors
Allen, Timothy https://orcid.org/0000-0001-7369-0901
Wedlake, Andrew
Gelzinyte, Elena
Gong, Charles
Goodman, Jonathan https://orcid.org/0000-0002-8693-9136
Description
Datasets and codes for the recreation and deployment of predictive toxicology models developed to predict 79 human molecular initiating events (MIEs) using neural networks (DOI: 10.1039/D0SC01637C).
Python codes are included to generate molecular fingerprints, generate chemical clusters, build models and recall those models.
Model files generated during clustered cross-validation and for comparison to structural alerts and random forests are also included.
Input biological data used in model construction is included for reference as complete datasets (Total), files divided as training and test for comparison to other models (Comparison), and clustered files used in clustered cross-validation (Clustered).
Version
Software / Usage instructions
Python 3
Keras
TensorFlow
Keywords
Artificial Intelligence, Machine Learning, Predictive Toxicology, Molecular Initiating Event
Publisher
Sponsorship
The authors acknowledge the financial support of Unilever.