Harnessing Artificial Intelligence for Next-Generation Predictive Toxicology

Predictive toxicology is a crucial field that focuses on assessing the potential toxic effects of chemical compounds, playing an essential role in drug development and chemical safety. The rapid evolution of artificial intelligence (AI) offers unprecedented opportunities to transform predictive toxicology. By leveraging machine learning algorithms and big data analytics, AI has the potential to improve accuracy in toxicity predictions and reduce the reliance on traditional, time-consuming, and expensive animal testing. Recent studies have demonstrated AI's potential in analyzing complex biological data, predicting adverse effects, and understanding the underlying mechanisms of toxicity. However, the integration of AI in predictive toxicology still presents challenges such as model interpretability, data quality, and cross-disciplinary collaboration.

This Research Topic aims to explore the applications of artificial intelligence in advancing the field of predictive toxicology. The goal is to examine how AI can enhance the prediction of toxicological outcomes, improve risk assessment processes, and facilitate the transition to more ethical and efficient toxicology practices. We seek to address critical questions related to the development of AI models for toxicity prediction, their validation and optimization, and how they can be effectively integrated into current regulatory frameworks. By examining these objectives, we aim to understand the transformative impact of AI on predictive toxicology.

To gather further insights in this transformative space, we welcome articles addressing, but not limited to, the following themes:

AI algorithms for toxicity prediction

Data integration and management in AI-driven toxicology

Ethical considerations in AI applications

Case studies of AI in drug safety assessment

Challenges and opportunities in regulatory adoption

We also invite those interested in contributing reviews, case reports, and original research articles on these themes.