Toxicity prediction of chemicals using OECD test guideline data with graph-based deep learning models
Daehwan Hwang, Changwon Lim · Korean Journal of Applied Statistics · 2024
In this paper, we compare the performance of graph-based deep learning models using OECD test guideline (TG) data.OECD TG are a unique tool for assessing the potential effects of chemicals on health and environment.but many guidelines include animal testing.Animal testing is time-consuming and expensive, and has ethical issues, so methods to find or minimize alternatives are being studied.Deep learning is used in various fields using chemicals including toxicity prediciton, and research on graph-based models is particularly active.Our goal is to compare the performance of graph-based deep learning models on OECD TG data to find the best performance model on there.We collected the results of OECD TG from the website eChemportal.orgoperated by the OECD, and chemicals that were impossible or inappropriate to learn were removed through pre-processing.The toxicity prediction performance of five graph-based models was compared using the collected OECD TG data and MoleculeNet data, a benchmark dataset for predicting chemical properties.