Work

Multitask Learning for Six-Pack Toxicity Prediction

Year: 2025

Type: conference-paper

Abstract: The assessment of the six-pack toxicity, the crucial six systems and organ toxicities, is vital for ensuring the safe use of chemicals.Computational models capable of providing reliable predictions are acceptable for regulatory use to replace animal testing.However, data scarcity issues hindered the development of prediction models.This study proposed the first application of multitask learning to the six-pack toxicity for addressing data scarcity issues.Five algorithms were implemented and compared.Results showed that the distinct chemical space of tasks impedes the learning of shared representation of conventional algorithms, with performance worse than baseline models.In contrast, the MTForestNet algorithm built on a biological readacross concept performed best, with 3.1% and 3.3% improvement on AUC and accuracy, respectively.These findings demonstrate that biologically informed multitask learning can effectively overcome data scarcity and enhance toxicity prediction.

Source: Annals of Computer Science and Information Systems

DOI: 10.15439/2025f1171

Authors: Chun-Wei Tung, Chia-Chi Wang, Run-Hsin Lin, Shan-Shan Wang

Institutions: National Health Research Institutes, National Taiwan University

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: National Science and Technology Council