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In silico prediction of potential drug‐induced nephrotoxicity with machine learning methods

https://doi.org/10.1002/jat.4331
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Outgoing claims: the papers this work cites, builds on, or argues with.

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PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints
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Drug-Induced Nephrotoxicity
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Support-Vector Networks
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Nephrotoxicity of over-the-counter analgesics, natural medicines, and illicit drugs
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Acute Taxol nephrotoxicity: Histological and ultrastructural studies of mice kidney parenchyma
inferredcites →https://doi.org/10.1016/s0025-7125(05)70541-1openalex-snapshot
DRUG-INDUCED NEPHROTOXICITY
inferredcites →https://doi.org/10.1016/s0270-9295(03)00094-9openalex-snapshot
Toxic nephropathy: environmental chemicals
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Nephrotoxicity of immunosuppressive drugs: Long-term consequences and challenges for the future
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In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods
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Designing around Structural Alerts in Drug Discovery
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Estimation of ADME Properties with Substructure Pattern Recognition
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Naïve Bayes Classification Using 2D Pharmacophore Feature Triplet Vectors
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Random Forests
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New insights into the mechanism of aminoglycoside nephrotoxicity: an integrative point of view
inferredcites →https://doi.org/10.1038/ki.2015.115openalex-snapshot
Phenotype standardization for drug-induced kidney disease
inferredcites →https://doi.org/10.1038/ncpneph0076openalex-snapshot
Drug-associated renal dysfunction and injury
inferredcites →https://doi.org/10.1038/srep12337openalex-snapshot
Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods
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Are 2D fingerprints still valuable for drug discovery?
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Tubulointerstitial Diseases
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The SIDER database of drugs and side effects
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The ChEMBL database in 2017
inferredcites →https://doi.org/10.1093/nar/gkx1037openalex-snapshot
DrugBank 5.0: a major update to the DrugBank database for 2018
inferredcites →https://doi.org/10.1093/toxsci/kfz021openalex-snapshot
A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro
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Drug-induced kidney disease in the ICU: mechanisms, susceptibility, diagnosis and management strategies
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Nearest neighbor pattern classification
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Renal safety of adefovir dipivoxil in patients with chronic hepatitis B: Two double-blind, randomized, placebo-controlled studies
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XGBoost
inferredcites →https://doi.org/10.1155/2020/3431290openalex-snapshot
Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients
inferredcites →https://doi.org/10.1159/000169072openalex-snapshot
Increased Nephrotoxicity of Combination Taxol and Cisplatin Chemotherapy in Gynecologic Cancers as Compared to Cisplatin Alone
inferredcites →https://doi.org/10.1186/1471-2105-15-s16-s16openalex-snapshot
Supervised prediction of drug-induced nephrotoxicity based on interleukin-6 and -8 expression levels
inferredcites →https://doi.org/10.1186/s13065-015-0139-7openalex-snapshot
Identification of structural alerts for liver and kidney toxicity using repeated dose toxicity data
inferredcites →https://doi.org/10.1186/s13321-020-00445-4openalex-snapshot
One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome
inferredcites →https://doi.org/10.1371/journal.pone.0015939openalex-snapshot
TCM Database@Taiwan: The World's Largest Traditional Chinese Medicine Database for Drug Screening In Silico
inferredcites →https://doi.org/10.1787/9789264085442-enopenalex-snapshot
Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models
inferredcites →https://doi.org/10.2174/1389200218666171108154419openalex-snapshot
Drug-Induced Nephrotoxicity: Pathogenic Mechanisms, Biomarkers and Prevention Strategies
inferredcites →https://doi.org/10.2215/cjn.00150118openalex-snapshot
Pharmacology behind Common Drug Nephrotoxicities
inferredcites →https://doi.org/10.2215/cjn.11571017openalex-snapshot
Nephrotoxicity and Chinese Herbal Medicine
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.5281/zenodo.3607804openalex-snapshot
Comparison between XGBoost, LightGBM and CatBoost Using a Home Credit Dataset
inferredcites →https://doi.org/10.5860/choice.35-2146openalex-snapshot
Comprehensive toxicology

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Kidney Organoids in Drug Development: Integrating Technological Advances and Standardization for Effective Implementation
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Prediction of IDO1 Inhibitors by a Fingerprint‐Based Stacking Ensemble Model Named IDO1Stack
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In Silico Prediction of ERRα Agonists Based on Combined Features and Stacking Ensemble Method
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Projection-based molecular feature maps for CNN-driven nephrotoxicity prediction
inferred← citeshttps://doi.org/10.1007/s11030-025-11376-3openalex-snapshot
Prediction of drug-induced nephrotoxicity based on deep learning algorithm and molecular fingerprints
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Predicting Renal Toxicity of Compounds with Deep Learning and Machine Learning Methods
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DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods
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Comprehensive Review on the Toxicity of Five Main AQ Constituents from Rhubarb: Mechanisms, Challenges and Future Perspectives
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MACHINE LEARNING-BASED VIRTUAL SCREENING, MOLECULAR DOCKING, DRUG-LIKENESS, PHARMACOKINETICS AND TOXICITY ANALYSES TO IDENTIFY NEW NATURAL INHIBITORS OF THE GLYCOPROTEIN SPIKE (S1) OF SARS-CoV-2
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