Harnessing Artificial Intelligence For Next-Generation Predictive Toxicology And Transforming Drug Discovery Processes: A Review Of Recent Literature

Abstract

Background:
Artificial intelligence (AI) is revolutionizing drug discovery by enhancing predictive accuracy, improving drug design, and personalizing treatments. AI technologies, particularly machine learning (ML) and deep learning, are increasingly applied to various stages of drug development, including predictive toxicology, drug optimization, and identifying new therapeutic targets. Despite its promise, challenges such as data quality, model interpretability, and regulatory acceptance remain.

Aim:
This systematic review explores the impact of AI on drug discovery, focusing on predictive toxicology, drug design, and personalized medicine. It evaluates how AI technologies accelerate drug development, improve accuracy, and reduce costs, while addressing challenges and opportunities in their integration.

Method:
A comprehensive search was conducted in electronic databases (PubMed, Scopus, Google Scholar, Web of Science, SpringerLink) for studies published between 2020 and 2024. Ten primary studies were selected based on their relevance to AI applications in drug discovery. A quality assessment of the selected studies was performed, followed by data synthesis to identify key themes and trends related to AI’s role in drug development.

Results:
The review found that AI plays a key role in predictive toxicology, optimizing drug design, and facilitating personalized medicine. AI models reduce development costs, improve efficacy, and accelerate drug candidate identification. However, challenges related to data integration, model transparency, and regulatory approval remain.

Conclusion:
AI is transforming drug discovery by improving efficiency, precision, and safety. While challenges remain, AI’s potential to revolutionize drug development is immense, especially in predictive toxicology, drug design, and personalized treatments.