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Related Experiment Video

Updated: May 20, 2025

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro

Published on: January 31, 2022

BoostDILI: Extreme Gradient Boost-Powered Drug-Induced Liver Injury Prediction and Structural Alerts Generation.

Hillul Chutia¹, Gori Sankar Borah², Hridoy Jyoti Mahanta^1,3

¹CSIR-North East Institute of Science and Technology, Jorhat 785006, India.

Chemical Research in Toxicology

|April 17, 2025

Summary

This summary is machine-generated.

This study introduces BoostDILI, a machine learning model predicting drug-induced liver injury (DILI) using XGBoost. It identifies structural alerts for DILI risk, aiding safer drug development.

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Area of Science:

Pharmacology
Computational Chemistry
Toxicology

Background:

Drug-induced liver injury (DILI) is a major safety concern leading to drug withdrawals.
Current DILI prediction methods are complex and time-consuming.
Accurate early DILI prediction is vital for pharmaceutical development.

Purpose of the Study:

Develop a machine learning model for early DILI prediction.
Evaluate the utility of public datasets for DILI prediction in FDA-approved drugs.
Generate structural alerts to enhance model explainability and identify DILI-causing substructures.

Main Methods:

Developed an extreme gradient boosting (XGB) machine learning model (BoostDILI).
Utilized four public datasets and integrated RDKit and Mordred features.
Employed sequential feature selection and Bayesian statistics for structural alert identification.

Main Results:

The BoostDILI model achieved 0.70 5-fold cross-validation accuracy.
Successfully validated the model on FDA-approved drug datasets (DILIst, DILIRank).
Identified key structural alerts associated with DILI risk.

Conclusions:

BoostDILI provides a reliable tool for preclinical DILI risk assessment.
Structural alerts enhance understanding of DILI mechanisms.
The model and data are publicly available to support further research.