Recent progress in machine learning approaches for predicting carcinogenicity in drug development

Area of Science:

• Pharmacology
• Computational Toxicology
• Drug Development

Background:

• Traditional carcinogenicity prediction methods (in vivo, in vitro assays) are resource-intensive and have limitations.
• Drug development faces challenges in accurate and efficient safety assessment.
• Machine learning (ML) offers a transformative approach to these challenges.

Purpose of the Study:

• To review the impact of ML on carcinogenicity prediction in drug development.
• To explore the integration of ML, deep learning, and AI in drug safety assessments.
• To highlight ML's role in overcoming traditional method limitations.

Main Methods:

• Review of existing literature on ML applications in drug development safety.
• Analysis of traditional carcinogenicity assessment techniques.
• Exploration of AI and deep learning integration in predictive toxicology.

Main Results:

• ML significantly enhances predictive accuracy and efficiency in carcinogenicity assessment.
• AI and deep learning are versatile tools applicable from early screening to clinical trials.
• ML addresses data interpretation, ethical, and regulatory challenges.

Conclusions:

• ML methodologies are revolutionizing carcinogenicity prediction and drug safety assessment.
• The integration of ML, deep learning, and AI offers innovative solutions to traditional method limitations.
• ML adoption is crucial for advancing efficient and ethical drug development.