bioRxivBioinformaticsPreprint
ReproTox-KG: Toxicology Knowledge Graph for Structural Birth Defects
John Erol EvangelistaiD, Daniel J. B. ClarkeiD, Zhuorui XieiD, Giacomo B. MarinoiD, Vivian UttiiD, Taha Mohseni AhooyiiD, Sherry L. JenkinsiD, Deanne M. TayloriD, Cristian BologaiD, Jeremy J. YangiD, Jessica BinderiD, Praveen Kumar IssaciD, Christophe Gerard LambertiD, Jeffrey Sean GretheiD, Eric Wenger, Tudor Ionel OpreaiD, Bernard de BonoiD, Avi Ma’ayaniD
Abstract
Birth defects are functional and structural abnormalities that impact 1 in 33 births in the United States. Birth defects have been attributed to genetic as well as other factors, but for most birth defects there are no known causes. Small molecule drugs, cosmetics, foods, and environmental pollutants may cause birth defects when the mother is exposed to them during pregnancy. These molecules may interfere with the process of normal fetal development. To characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with an initial focus on associations between birth defects, drugs, and genes. Specifically, to construct ReproTox-KG we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression data, known drug targets, genetic burden scores for all human genes, and placental crossing scores for all small molecules in ReproTox-KG. Using the data stored within ReproTox-KG, we scored 30,000 preclinical small molecules for their potential to induce birth defects. Querying the ReproTox-KG, we identified over 500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG is provided as curated tables and via a web-based user interface that can enable users to explore the associations between birth defects, approved and preclinical drugs, and human genes.
- Citations
- 1
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Machine learning on drug-specific data to predict small molecule teratogenicity
Anup P. Challa, Andrew L. Beam, … David Michael Aronoff·Reproductive ToxicologyPeer-reviewed·May 16, 2020
- Every Cure Knowledge Graph: A Unified Biomedical Knowledge Graph for Drug Repurposing
Piotr Kaniewski, Kathleen Carter, … Lee Lancashire·bioRxiv·Aug 31, 2026
- Deciphering the links between metabolism and health by building tailored knowledge graphs: application to endometriosis and persistent pollutants
Meije Mathé, Guillaume Laisney, … Clément Frainay·bioRxiv·Mar 16, 2026
- NeurotoxKb: compilation, curation and exploration of a knowledgebase of environmental neurotoxicants specific to mammals
J. Ravichandran, B. S. Karthikeyan, … A. Samal·bioRxiv·Jan 12, 2021
- KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description
Chunyu Ma, Zhihan Zhou, … David Koslicki·GigaSciencePeer-reviewed·Dec 28, 2022
- Drug-centric knowledge graph: representing therapeutic modality, pharmacology and multiscale biology
Alon Bar, Michael Shapiro, … Flavio Dormont·bioRxiv·Oct 16, 2026