Novel Algorithms and Models in Computational Toxicology and Pollution: Towards More Accurate Toxicity and Pollution Prediction

Dear Colleagues,

Rapid industrialization and chemical production have led to increasing environmental contamination, necessitating more efficient and scalable approaches to assessing toxicity and pollution. Traditional experimental methods, while important, are often limited in scope, costly, and time-consuming. Computational toxicology offers powerful alternatives by applying machine learning, predictive algorithms, and data-driven models to better understand pollutant behavior, toxicity mechanisms, and environmental risks.

The Special Issue, “Novel Algorithms and Models in Computational Toxicology and Pollution: Towards More Accurate Toxicity and Pollution Prediction,” focuses on advancing computational approaches for assessing toxicological risks and environmental pollution. Emphasis is placed on novel algorithms, machine learning models, and data-driven frameworks that enhance prediction accuracy, interpretability, and practical application in chemical risk assessment. This collection seeks to complement experimental studies, reduce reliance on animal testing, and provide efficient tools for environmental decision-making. Positioned within the expanding literature on computational toxicology, the issue highlights current progress while addressing persisting challenges such as model validation and real-world applicability. It aims to bridge theory and practice, offering a roadmap for future research and improved pollution management.

This Special Issue welcomes contributions that advance novel computational frameworks for toxicity and pollution prediction, improve model validation and reliability, and demonstrate real-world applications in environmental health and pollution management. Topics of interest include, but are not limited to predictive modeling for toxicity, fate and transport of pollutants, integration of omics and big data, cheminformatics approaches, and interdisciplinary methods bridging toxicology and environmental science.

We invite original research articles, reviews, and short communications.

Dr. Abdullahi Garba Usman
Guest Editor

Manuscript Submission Information

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