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

Nguyen Quoc Khanh Le^1,2,3,4, Thi-Xuan Tran⁵, Phung-Anh Nguyen^6,7

¹Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.

Expert Opinion on Drug Metabolism & Toxicology

|May 14, 2024

Summary

Machine learning (ML) is revolutionizing drug development by improving carcinogenicity prediction. This approach enhances safety assessments, overcoming limitations of traditional methods for more efficient and accurate drug discovery.

Keywords:

Artificial intelligence carcinogenicity prediction computational toxicology drug development machine learning predictive modeling safety assessment toxicogenomics

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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.