Call for papers - Machine learning for predictive toxicology

Guest Editor

Duc Nguyen, PhD, University of Kentucky, USA

Submission Status: Open | Submission Deadline: 14 March 2025

BMC Pharmacology and Toxicology is calling for submissions to our Collection on Machine learning for predictive toxicology. Machine learning and deep learning enable efficient, ethical computational modeling for assessing chemical and drug toxicity, reducing the cost and time of drug development. We are inviting submissions focusing on, but not limited to, refining predictive models, improving chemical risk assessment accuracy, and enhancing toxicological screening efficiency, including QSAR, read-across, expert systems, data integration, and novel computational methods in drug development and safety.

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Meet the Guest Editor

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Duc Nguyen, PhD, University of Kentucky, USA

Professor Duc Nguyen is an expert at the intersection of mathematics, molecular bioscience, and data science, and serves as an Associate Professor in the Department of Mathematics at the University of Kentucky. His research focuses on three areas: developing mathematical models for molecular bioscience and biophysics, designing machine learning architectures to enhance learning accuracy, and constructing high-order methods for scientific computing. His work has been supported by three NSF grants, Pfizer, and Bristol-Myers Squibb. Professor Nguyen's significant impact is demonstrated through his success in the D3R Grand Challenges, a prestigious competition in computer-aided drug design (CADD), where his models were ranked first in several categories. Recognized among the top 2% of the world’s most-cited researchers, his contributions advance scientific understanding and innovation, specializing in math and AI-driven drug discovery.

About the Collection

BMC Pharmacology and Toxicology is calling for submissions to our Collection on Machine learning for predictive toxicology.

Machine learning is a powerful approach to identify and assess the potential toxicity of chemicals and drugs and to finally improve human health and safety. This together with deep learning make computational modeling feasible, thereby remarkably reducing the cost and time and avoiding ethical concerns of animal or clinical tests in traditional drug development. However, refining predictive models, improving the assessed accuracy of chemical risk, enhancing the efficiency of toxicological screening, and increasing human benefits requires more research.

We are particularly interested in research that leverages machine learning to enhance predictive toxicology. Topics of interest include, but are not limited to:

Innovative computational approaches for predictive toxicology, including quantitative structure-activity relationships (QSAR), read-across, and expert systems
Advanced data integration, analysis, and interpretation of complex toxicological conditions
Novel computational methods for chemical risk assessment of drug and chemical safety
Application of computational models in drug development or chemical safety

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Machine learning for predictive toxicology" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.