Repository logo
 

Research data supporting "Neural network activation similarity: A new measure to assist decision making in chemical toxicology"


Change log

Authors

Wedlake, Andrew 
Gelzinyte, Elena 
Gong, Charles 

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.
Relationships
Supplements: