Journal Article

A Multitask Active Learning Framework with Probabilistic Modeling for Multi-Species Acute Toxicity Prediction

Han, Tianyu, Lin, Ying, Zhao, Yanpeng, Bo, Xiaochen, Wang, Jingjing, Zan, Peng, Yu, Lu, He, Song

2026

Overview

Predicting acute toxicity across species is essential for early-stage drug safety evaluation. While recent efforts have primarily focused on improving predictive accuracy, they often fail to address two critical issues: the substantial divergence in toxicity mechanisms among different species, and the inherent noise present in experimental data. To bridge this gap, we introduce a Probabilistic Multitask Active Learning (PMAL) framework for multi-species acute toxicity prediction. Our framework integrates two key modules: a Probabilistic Multitask Learning (PML) component which jointly models the predictive distributions of multiple toxicity endpoints from a probabilistic viewpoint, and an Uncertainty-based Active Learning (UAL) component which strategically selects the most informative compounds for experimental annotation based on predictive uncertainty. Empirical evaluations demonstrate that PMAL surpasses state-of-the-art methods and is capable of providing well-calibrated uncertainty estimates for small molecules across diverse toxicity endpoints. Beyond advancing multi-species toxicity prediction, the core design principles of PMAL offer a generalizable paradigm for learning in noisy multi-task environments.

• Publisher: MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
• Subject: Accuracy / active learning / acute toxicity prediction / Analysis / Animals / Architecture / Calibration / Computational linguistics / Datasets / Deep learning / Drug-Related Side Effects and Adverse Reactions / Epistemology / Humans / Labeling / Language processing / Machine Learning / Models, Statistical / multi-task learning / Natural language interfaces / Neural networks / Optimization / Physiology / probabilistic model / Toxicity / Toxicity Tests, Acute - methods / Toxicology