Data-driven identification of structural alerts for mitigating the risk of drug-induced human liver injuries
Open accessAbstract
BACKGROUND: The use of structural alerts to de-prioritize compounds with undesirable features as drug candidates has been gaining in popularity. Hundreds of molecular structural moieties have been proposed as structural alerts. An emerging issue is that strict application of these alerts will result in a significant reduction of the chemistry space for new drug discovery, as more than half of the oral drugs on the market match at least one of the alerts. To mitigate this issue, we propose to apply a rigorous statistical analysis to derive/validate structural alerts before use. METHOD: To derive human liver toxicity structural alerts, we retrieved all small-molecule entries from LiverTox, a U.S. National Institutes of Health online resource for information on human liver injuries induced by prescription and over-the-counter drugs and dietary supplements. We classified the compounds into hepatotoxic, nonhepatotoxic, and possible hepatotoxic classes, and performed detailed statistical analyses to identify molecular structural fragments highly enriched in the hepatotoxic class beyond random distribution as structural alerts for human liver injuries. RESULTS: We identified 12 molecular fragments present in multiple marketed drugs that one can consider as common "drug-like" fragments, yet they are strongly associated with drug-induced human liver injuries. Thus, these fragments may be considered as robust hepatotoxicity structural alerts suitable for use in drug discovery screening programs. CONCLUSIONS: The use of structural alerts has contributed to the identification of many compounds with potential toxicity issues in modern drug discovery. However, with a large number of structural alerts published to date without proper validation, application of these alerts may restrict the chemistry space and prevent discovery of valuable drugs. To mitigate this issue, we showed how to use statistical analyses to develop a small, robust, and broadly applicable set of structural alerts. Graphical abstractHepatotoxic-specific filters for flagging high risk compounds.
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Development of new experimental techniques such as RNA-Seq provides new types of data for in vitro assessment of toxicities that can be used for QSAR modeling.
“It should be emphasized that development of new experimental techniques such as deep-sequencing RNA-Seq,90 provides new types of data for in vitro assessment of toxicities that can also be used for QSAR modeling.91”
From QSAR without borders (2020) · cites this paper as [91]
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Liver Safety of the Dual Neurokinin-1/-3 Receptor Antagonist Elinzanetant
Lewis, J. H., Andrade, R. J., Larrey, D. G., Horsmans, Y., Anderson, R. A., Trajanovic, M., Groettrup‐Wolfers, E., & Zuurman, L. (2026) · Drug Safety · doi:10.1007/s40264-026-01663-9 · Cited by 2 · see citing works
Jin, W., Zhu, J., & Zhou, A. (2019) · Phosphorus, sulfur, and silicon and the related elements · doi:10.1080/10426507.2019.1686376 · Cited by 2 · see citing works
Wang, Z., Lin, Z., Lin, W., Yang, M., Zeng, M., & Tan, K. C. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3664098 · Cited by 9 · see citing works
Zhao, Y., Lu, Y., Fu, T., Zhang, J., & Tang, J. (2026) · Basic Research in Cardiology · doi:10.1007/s00395-025-01156-3 · Cited by 2 · see citing works
QSAR modeling for predicting drug-induced liver injury
Liao, T.-J., Zhao, J., & Chen, M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15339-6.00009-6
Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605398
Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605401
Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605389
Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605386
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Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605395
Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605404
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TheProperty: Euclidean 3D Molecular Representations for Explainable ADMET Prediction
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Semantic knowledge improves molecular machine learning for chemical toxicity prediction
Albi, G., Dagliati, A., Bellazzi, R., & Romano, J. D. (2026) · iScience · doi:10.1016/j.isci.2026.116937
Biological interaction fingerprints improve prediction of drug-induced steatosis
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- Authors
- Liu, R., Yu, X., & Wallqvist, A.
- Affiliations
- Telemedicine & Advanced Technology Research Center (US)
- Corresponding author
- Liu, Ruifeng
- Wallqvist, Anders
- Venue
- Journal of Cheminformatics
- Published
- 2015-02-11
- Volume
- 7
- Issue
- 1
- Pages
- 4-4
- ISSN
- 1758-2946 (ISSN-L)
- PMC
- PMC4339691
- OpenAlex
- W2097851570
- Cited by
- 92
- References
- 26
- Citations by year
- 2026: 7 · 2025: 3 · 2024: 4 · 2023: 9 · 2022: 13 · 2021: 16 · 2020: 9 · 2019: 7 · 2018: 5 · 2017: 10 · 2016: 7 · 2015: 2
- FWCI
- 9.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Cheminformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Defense
- Defense Threat Reduction Agency (CBCall14-CBS-05-2-0007)
- Medical Research and Materiel Command (CBCall14-CBS-05-2-0007)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Drug-Induced Hepatotoxicity and Protection
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Keywords
- structural alerts, drug-induced liver injury, hepatotoxicity, molecular fragments, drug-likeness, drug discovery screening, toxicity prediction, small molecule drugs
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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