KidneyTox_v1.0 Enables Explainable Artificial Intelligence Prediction of Nephrotoxicity in Small Molecules
AI-Driven Nephrotoxicity Prediction: Unlocking Safer Drug Discovery
Our innovative platform, KidneyTox_v1.0, integrates advanced AI with cheminformatics to deliver accurate and explainable predictions of drug-induced kidney toxicity, accelerating preclinical development and enhancing patient safety.
Transforming Drug Safety & Development Workflows
Leveraging KidneyTox_v1.0, enterprises can significantly reduce R&D costs, minimize late-stage failures, and bring safer, more effective drugs to market faster. Our AI provides actionable insights for medicinal chemists and toxicologists.
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Model Accuracy
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Faster Preclinical Screening
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology
This section details the robust AI/ML framework, descriptor generation, and model development behind KidneyTox_v1.0. Discover how our approach ensures high accuracy and interpretability.
AI Model Development Pipeline
Data Curation & Pre-processing
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Descriptor Calculation (Mordred)
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Feature Selection (Information Gain)
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Hyperparameter Optimization (Optuna)
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Random Forest Model Training
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SHAP-based Explainability
Optimal Model Accuracy
94.25% The Random Forest Classifier achieved exceptional accuracy on the training set, demonstrating its predictive power for nephrotoxicity.Key Descriptors Identified
10+ More than 10 molecular descriptors, including BCUT, autocorrelation, and dipole-related types, were identified as crucial for prediction.Chemical Space Analysis
Explore the diversity of the dataset and the structural uniqueness across compounds. This analysis confirms the broad applicability and robustness of KidneyTox_v1.0.
Dataset Diversity
565 The dataset comprises 565 chemically diverse small molecules, including 287 toxic and 278 non-toxic drugs.| Property | Average Value | Significance |
|---|---|---|
| LogP | 1.81 | Moderately lipophilic, indicating good bioavailability. |
| MW | 416.87 | Small to medium-sized molecules, typical for drug candidates. |
| nRings | ~2 | Diverse ring systems, contributing to structural complexity. |
| nRB | ~7 | Moderate flexibility, crucial for target binding. |
Explainable AI (XAI) & Interpretability
Understand the molecular features driving nephrotoxicity predictions. Our SHAP-based analysis provides transparent, actionable insights for drug design.
SHAP Insights
Molecular Electronegativity Higher atomic electronegativity (BCUTs-1h) in certain regions positively contributes to predicted toxicity.Case Study: Lansoprazole & Ciprofloxacin
"SHAP waterfall plots clearly demonstrate how specific descriptors, like AETA_eta_F and BCUTs-1h, contribute significantly to the 'Toxic' classification for known nephrotoxic drugs, providing actionable chemical alerts."
Figure 4A & 4B, Scientific Reports (2026)
Case Study: Clindamycin & Simvastatin
"Conversely, for 'Non-toxic' compounds like Clindamycin and Simvastatin, descriptors such as SpMax_A provide significant negative contributions, reinforcing the model's ability to differentiate safety profiles."
Figure 4C & 4D, Scientific Reports (2026)
qRASAR Descriptors & Predictive Tool
Learn about the development of qRASAR models and the open-access KidneyTox_v1.0 platform for real-time nephrotoxicity prediction.
Best qRASAR Model F1-Score
0.7327 The EUC (Selected) qRASAR model achieved a strong F1-score, balancing sensitivity and specificity.| Descriptor | Description |
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| MaxWtSim(EUC) | Maximum weighted similarity to active analogs, combining chemical similarity and experimental activity. |
| MaxNeg(EUC) | Maximum similarity to inactive analogs, safeguarding against false positives. |
| gm(EUC) | Banerjee-Roy coefficient: geometric mean of local error contributions, quantifying inconsistency. |
KidneyTox_v1.0: Open-Access XAI Platform
"Our web-based tool, KidneyTox_v1.0, provides instant nephrotoxicity predictions, integrates SHAP for interpretability, and helps users understand molecular contributions, driving rational drug design."
https://kidneytoxv1.streamlit.app/
Quantify Your AI Impact
Use our interactive calculator to estimate the potential cost savings and efficiency gains KidneyTox_v1.0 could bring to your organization's drug discovery pipeline.
Estimated Annual Savings
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Annual Hours Reclaimed
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Our Phased Approach to Enterprise AI Integration
A strategic roadmap for seamlessly integrating KidneyTox_v1.0 into your R&D pipeline, ensuring maximum impact and rapid value realization.
Phase 1: Discovery & Scoping
Initial assessment of current drug screening processes, identification of key integration points, and custom configuration of KidneyTox_v1.0 to align with your specific research objectives.
Phase 2: Integration & Customization
Seamless integration of the XAI platform with your existing cheminformatics and drug discovery databases. Customization of models for proprietary datasets and specific toxicity endpoints.
Phase 3: Validation & Training
Comprehensive validation against internal benchmarks and training of your scientific teams on advanced features, SHAP interpretability, and optimal usage for novel compound screening.
Phase 4: Deployment & Optimization
Full production deployment of KidneyTox_v1.0. Continuous monitoring, performance optimization, and ongoing support to ensure sustained impact on your drug development initiatives.
Ready to Accelerate Your Drug Discovery?
Book a personalized strategy session to explore how KidneyTox_v1.0 can revolutionize your nephrotoxicity prediction capabilities and bring safer drugs to market faster.
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