ToxiVerse: chemical bioprofiling, toxicity data sharing and customizable predictive modeling

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ID: 326262
2026
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Abstract
Abstract Motivation Chemical toxicity assessment is critical for drug development and environmental safety. Computational models have emerged as a promising alternative to animal testing and now play a significant role in efficiently evaluating new chemicals. To address the urgent need for user-friendly machine learning tools in computational toxicology, we developed ToxiVerse, a public web-based platform. Results ToxiVerse provides automatic chemical bioprofiling, curated toxicity datasets, and a predictive modeling interface designed for researchers who lack programming expertise. The platform comprises three integrated modules: (i) Bioprofiler, which provides chemical descriptors by combining chemical-bioactivity data from PubChem assays with a machine learning-based data gap-filling procedure; (ii) Database, which hosts ∼50 000 curated chemicals covering diverse toxicity endpoints; and (iii) Cheminformatics, which enables dataset upload, chemical curation, and automatic generation of quantitative structure–activity relationship models for toxicity prediction. Availability The tool is accessible at www.toxiverse.com, and source code is available at https://github.com/zhu-research-group/toxiverse. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7204010512 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Prasannavenkatesh Durai, Daniel P. Russo, Yitao Shen, Tong Wang, Elena Chung, Lang Li, Hao Zhu
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag617
URL
https://doi.org/10.1093/bioinformatics/btag617
Keywords Keywords not found

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