Amesformer: a graph transformer neural network for mutagenicity prediction

07 October 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The Ames mutagenicity test is a gold standard assay for the safety assessment of new chemicals. However, many in silico models rely on challenging-to-interpret ensemble strategies and molecular fingerprint data which neglects gestalt molecular structure. To improve upon these models, we propose AmesFormer, a graph transformer neural network which shows state-of-the-art performance when paired with our new Ames dataset. We briefly review the current state of Ames modelling with a focus on graph neural networks. We then benchmark AmesFormer on a standardised test dataset against 22 other Ames models, achieving state of the art (SOTA) performance. We then uniquely report the calibration performance of our model and attempts to improve it using temperature scaling. We support our findings with reference to other models from the literature and with developments in machine learning (ML) and graph theory. Overall, we present a high-performance, accessible, and open-source computational model for Ames mutagenicity, with significant potential for regulatory and drug development applications

Keywords

Ames test
Mutagenicity
QSAR
GNN
Transformer

Supplementary weblinks

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.