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Large-scale Modeling of Multi-Species Acute Toxicity Endpoints using Consensus of Multi-Task Deep Learning Methods

This repository contains multitask deep learning models developed using acute toxicity data, primarily focusing on the endpoints: lethal dose fifty (LD50); lethal dose low (LDLO); and toxic dose low (TDLO). Please note that the data was obtained from ChemIDPlus.

Results

Our best models are based on a consensus of best developed individual models. We compared our best models against the multi-task deep learning models by Sosnin et al. While they report models for a total of 29 toxicity endpoints, our models are based on a total of 59 endpoints. A total of 18 LD50 endpoints were in common. The results for these 18 endpoints are listed below. The performance measure reported is root mean squared error (lower is better).

species route cpds (ours) cpds (Sosnin et al) score (ours) scorea (Sosnin et al)
mouse intraperitoneal 36295 37202 0.41 0.41
mouse oral 23373 24355 0.39 0.42
mouse intravenous 16978 17742 0.43 0.43
rat oral 10190 10743 0.52 0.53
mouse subcutaneous 6769 7221 0.51 0.51
rat intraperitoneal 5021 5041 0.52 0.55
rat intravenous 2472 2538 0.52 0.54
rat subcutaneous 1896 2014 0.63 0.64
mouse unreported 1739 1804 0.47 0.51
rabbit skin 1495 1734 0.53 0.56
mammalb unreported 1129 1121 0.42 0.40
rabbit oral 894 910 0.58 0.58
rat skin 835 930 0.61 0.63
rat unreported 806 838 0.58 0.60
rabbit intravenous 792 764 0.59 0.68
guinea pig oral 793 799 0.66 0.70
rat oral 322 966 0.63 0.61
rat intraperitoneal 318 1029 0.52 0.43

a the scores are from the supplementary information of the original article; b the mammalian species and route are unspecified

Other Models

We also report single-task models using baseline methods: random forest and deep neural networks. The scripts used for modeling can be found under scripts/. An example notebooks/create_fold_data.ipynb to create the training and test sets by joining the descriptors and task details for different folds of cross-validation is provided.