Machine Learning Models for Predicting Liver Toxicity.

Area of Science:

• Pharmacology
• Toxicology
• Computational Biology

Background:

• Drug-induced liver toxicity is a primary cause of clinical trial failures and market withdrawals.
• Current in vivo animal testing for liver toxicity is expensive and time-consuming.
• Early prediction of liver toxicity is crucial for reducing drug development costs and failure rates.

Purpose of the Study:

• To review advances in machine learning (ML) for predicting human liver toxicity.
• To discuss the application of ML models in drug discovery for liver safety.
• To explore potential improvements in ML-based liver toxicity prediction.

Main Methods:

• Review of current literature on machine learning models for liver toxicity prediction.
• Analysis of the development and application of these models.
• Discussion of challenges and future directions in the field.

Main Results:

• Machine learning models show promise as an alternative to traditional animal testing.
• Various ML approaches have been developed and applied for predicting liver toxicity.
• The chapter synthesizes current knowledge on ML for liver safety assessment.

Conclusions:

• Machine learning offers a powerful tool for early prediction of drug-induced liver toxicity.
• Further development and validation of ML models can enhance drug safety and reduce development costs.
• Improved ML strategies are needed to refine liver toxicity prediction in drug discovery.