Abstract
Drug-induced nephrotoxicity remains a leading cause of kidney dysfunction, often with severe or even fatal outcomes. Computational approaches, in particular artificial intelligence (AI), offer a promising alternative by providing reliable, cost-effective, and ethically sound tools for assessing drug-induced nephrotoxicity. Thereby, potentially reducing reliance on animal testing. This study was driven by three core objectives: (i) to analyze the chemical space of compounds associated with drug-induced nephrotoxicity, (ii) to construct a robust supervised machine learning (ML) model for classification, followed by a quantitative Read-Across Structure-Activity Relationship (qRASAR) study, and (iii) to develop an open-access, eXplainable AI (XAI) platform named “KidneyTox_v1.0” (https://kidneytoxv1.streamlit.app/) for nephrotoxicity prediction. Beyond providing predictions, “KidneyTox_v1.0” offers interpretability through interactive SHAP-based waterfall plots, enabling both domain experts and non-experts to understand the contribution of molecular descriptors to toxicity outcomes. These modelling analyses will assist chemists in designing less nephrotoxic molecules in the future.
Data availability
Data are available in the manuscript, Supplementary material, and on the GitHub page (https://github.com/Amincheminform/KidneyTox_v1.0).
References
Perazella, M. A. Drug-induced acute kidney injury: diverse mechanisms of tubular injury. Curr. Opin. Crit. Care. 25, 550–557 (2019).
Zager, R. A. Acute renal failure: pathogenesis, diagnosis, and therapy. Semin Nephrol. 18, 524–534 (1998).
Connor, S., Roberts, R. A. & Tong, W. Drug-Induced kidney injury: challenges and opportunities. Toxicol. Res. 13, tfae119 (2024).
Xie, H. G., Wang, S. K., Cao, C. C. & Harpur, E. Qualified kidney biomarkers and their potential significance in drug safety evaluation and prediction. Pharmacol. Ther. 137, 100–107 (2013).
Kar, S. & Leszczynski, J. Open access in Silico tools to predict the ADMET profiling of drug candidates. Expert Opin. Drug Discov. 15, 1473–1487 (2020).
Escher, S. E. et al. Read-across for environmental risk assessment: approaches and challenges. Regul. Toxicol. Pharmacol. 106, 294–309 (2019).
Gong, Y. et al. In Silico prediction of potential drug-induced nephrotoxicity with machine learning methods. J. Appl. Toxicol. 42, 1639–1650 (2022).
Mater, A. C. & Coote, M. L. Deep learning in chemistry. J. Chem. Inf. Model. 59, 2545–2559 (2019).
Bhhatarai, B. et al. Opportunities and challenges using artificial intelligence in ADME/Tox. Nat. Mater. 18, 418–422 (2019).
Singh, P. et al. Artificial intelligence in nephrology: clinical applications and future directions. Kidney Med. 7, 100927 (2025).
Yousif, Z. & Awdishu, L. Drug-Induced acute kidney injury risk prediction models. Nephron 147, 44–47 (2023).
Su, R., Li, Y., Zink, D. & Loo, L. H. Supervised prediction of drug-induced nephrotoxicity based on interleukin-6 and – 8 expression levels. BMC Bioinformatics 15 (Suppl 16), S16 (2014).
Basile, A. O., Yahi, A. & Tatonetti, N. P. Artificial intelligence for drug toxicity and safety. Trends Pharmacol. Sci. 40, 624–635 (2019).
Rao, M. et al. Artificial intelligence and machine learning models for predicting Drug-Induced kidney injury in small molecules. Pharmaceuticals 17, 1550 (2024).
Banerjee, A. et al. Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure–activity relationship (q-RASAR) with the application of machine learning. Crit. Rev. Toxicol. 54, 659–684 (2024).
Amorim, A. M. B. et al. Advancing drug safety in drug development: bridging computational predictions for enhanced toxicity prediction. Chem. Res. Toxicol. 37, 827–849 (2024).
Shi, Y. et al. In Silico prediction and insights into the structural basis of drug induced nephrotoxicity. Front. Pharmacol. 12, 793332 (2022).
Toropov, A. A. et al. CORAL models for Drug-Induced nephrotoxicity. Toxics 11, 293 (2023).
Amin, S. A. et al. Structural insights and molecular profiling of a large set of diverse compounds targeting PPARγ: from comprehensive cheminformatics approach to tool development. SAR QSAR Environ. Res. 36, 443–461 (2025).
Moriwaki, H., Tian, Y. S., Kawashita, N., Takagi, T. & Mordred A molecular descriptor calculator. J. Cheminform. 10, 4 (2018).
Amin, S. A., Kar, S. & Piotto, S. pDILI_v1: A Web-Based machine learning tool for predicting Drug-Induced liver injury (DILI) integrating chemical space analysis and molecular fingerprints. ACS Omega. 10, 13502–13514 (2025).
Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Front. Bioinform. 2, 927312 (2022).
Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003).
Lai, J. P. et al. Tree-based machine learning models with optuna in predicting impedance values for circuit analysis. Micromachines 14, 265 (2023).
Yang, S. & Kar, S. Applicability domain for trustable predictions. Methods Mol. Biol. 2834, 131–149 (2025).
Kar, S., Roy, K. & Leszczynski, J. Applicability domain: A step toward confident predictions and decidability for QSAR modeling. In Computational Toxicology, 213–237 (Humana Press, New York, NY, 2018).
OECD. Guidance document on the validation of QSAR models. (2025). https://www.oecd.org/en/publications/guidancedocument-on-the-validation-of-quantitative-structure-activityrelationship-q-sar-models
Todeschini, R. & Consonni, V. Molecular Descriptors for Chemoinformatics (Wiley-VCH, 2009).
Banerjee, A. & Roy, K. How to correctly develop q-RASAR models for predictive cheminformatics. Expert Opin. Drug Discov. 19, 1017–1022 (2024).
Banerjee, A. & Roy, K. The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset. Sci. Rep. 14, 20812 (2024).
RDKit. Getting started in Python. (2025). https://www.rdkit.org/docs/GettingStartedInPython.html
DTC Lab Tools. Supplementary site. Accessible at: https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home
Acknowledgements
SK would like to thank the administration of the Dorothy and George Hennings College of Science, Mathematics, and Technology (HCSMT) at Kean University for providing research opportunities through release time and resources. SAA and SP sincerely acknowledge the University of Salerno, Italy for providing the research facilities.
Author information
Authors and Affiliations
Contributions
Sk Abdul Amin: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, review & editing. Supratik Kar: Conceptualization, Formal analysis, Resources, Supervision, Writing – original draft, review & editing. Stefano Piotto: Resources, Supervision, Writing – review & editing.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Amin, S.A., Kar, S. & Piotto, S. KidneyTox_v1.0 enables explainable artificial intelligence prediction of nephrotoxicity in small molecules. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35496-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-35496-4
