Modelling In vitro Mutagenicity Using Multi-Task Deep Learning and REACH Data | JoVE Visualize

Area of Science:

Computational toxicology
Chemical safety assessment
In vitro toxicology

Background:

Mutagenicity assessment under REACH regulation relies on a tiered approach of in vitro and in vivo testing.
Existing in vitro assays for mutagenicity assessment include bacterial gene mutation tests and mammalian cell assays.
Exploring correlations between in vitro assays can potentially improve predictive model performance.

Purpose of the Study:

To investigate the use of multi-task deep learning models for predicting chemical mutagenicity based on in vitro assay data.
To compare the performance of multi-task deep learning models against single-task models and classical machine learning methods.
To assess the generalizability of developed models using extensive external test sets.

Main Methods:

Compiled a large genotoxicity dataset (>12,000 substances) from REACH, ToxValDB, and literature.
Developed and evaluated various single-task and multi-task deep learning models, including graph neural networks.
Utilized classical machine learning techniques and chemical fingerprints for comparison.
Constructed external test sets for rigorous model validation.

Main Results:

Deep learning single-task models achieved 73-84% balanced accuracy in cross-validation for in vitro assays, outperforming classical methods by 2-8%.
Specific bacterial gene mutation and metabolic activation models showed 82-85% balanced accuracy, with 7-12% improvement.
Multi-task models demonstrated an average 8% higher cross-validation accuracy than single-task models for specific assays.
External validation showed 72-78% balanced accuracy for best models with sufficient data.
Graph neural network embeddings identified structural alerts and correlated structural moieties with genotoxicity outcomes.

Conclusions:

Multi-task deep learning models show promise for improving the accuracy and efficiency of in vitro mutagenicity assessments.
The developed models can predict genotoxicity and identify structure-activity relationships, aiding in chemical safety evaluations.
These computational approaches offer a valuable complement to traditional testing strategies under regulatory frameworks like REACH.