# KidneyTox_v1.0 enables explainable artificial intelligence prediction of nephrotoxicity in small molecules

## TL;DR

This paper introduces an AI tool called KidneyTox_v1.0 that predicts and explains kidney toxicity in drugs, helping reduce animal testing.

## Contribution

The novel contribution is an open-access XAI platform for nephrotoxicity prediction with interactive interpretability features.

## Key findings

- A robust ML model was developed for classifying nephrotoxic compounds.
- The platform provides SHAP-based explanations for toxicity predictions.
- The tool supports chemical design to reduce nephrotoxicity.

## 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.

The online version contains supplementary material available at 10.1038/s41598-026-35496-4.