Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules

The integrated model, which combines both physicochemical properties and off-target interaction data, significantly improved DIKI prediction accuracy compared to models that rely on either data type alone. This AI/ML model provides a promising early screening tool for identifying compounds with lower DIKI risk, facilitating safer drug development.

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Publication Details

Journal
Pharmaceuticals
Published
2024-11-19
DOI
https://doi.org/10.3390/ph17111550
Citations
9
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.25
Controls
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article

Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules

Amy H. Yang, Satjit Brar, Mohan Rao, Vahid Nassiri et al.
9 citations
Pharmaceuticals
Computational Drug Discovery Methods
3.25
article

Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules

Amy H. Yang, Satjit Brar, Mohan Rao, Vahid Nassiri, Clifford Sachs, Eric McDuffie, Sanjay Srivastava
article en
9 citations

Abstract

The integrated model, which combines both physicochemical properties and off-target interaction data, significantly improved DIKI prediction accuracy compared to models that rely on either data type alone. This AI/ML model provides a promising early screening tool for identifying compounds with lower DIKI risk, facilitating safer drug development.

PharmaceuticalsVol. 17(11)
Neurocrine Biosciences (United States) (US)
Good health and well-being
Openalex Percentile: Top 7%
Computational Drug Discovery Methods
3.25
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