BioMed Central, part of Springer Nature, has announced the launch of Machine learning for predictive toxicology, a new collection at BMC Pharmacology and Toxicology. This new collection aims to highlight the transformative impact of machine learning and deep learning in enhancing the safety and efficiency of drug discovery and development.
Toxicity accounts for nearly one-third of drug candidate attrition, significantly contributing to the high costs associated with drug development. Predictive toxicology seeks to uncover and mitigate the hazards posed by druggable chemicals, reduce failure rates, and ultimately improve clinical safety. By integrating preclinical and clinical toxicity data with advanced technologies, machine learning offers a promising alternative to traditional toxicology approaches, potentially reducing both time and cost without the ethical concerns associated with animal or clinical testing.
This collection will focus on research that leverages machine learning to refine predictive models, enhance chemical risk assessment accuracy, and improve the clinical safety of drugs. The integration of chemical, biological, and mechanistic data through AI-based modeling could revolutionize toxicity assessment, but challenges remain in modeling the multifaceted nature of toxicity across various platforms and interpreting the vast number of predictions generated.
Dr. Duc Nguyen, an esteemed Associate Professor in the Department of Mathematics at the University of Kentucky, will serve as the Guest Editor for this collection. Dr. Nguyen’s expertise spans mathematics, molecular bioscience, and data science. His research focuses on developing mathematical models for molecular bioscience and biophysics, designing machine learning architectures to enhance learning accuracy, and constructing high-order methods for scientific computing. Supported by three NSF grants, Pfizer, and Bristol-Myers Squibb, Dr. Nguyen’s contributions have been recognized in the D3R Grand Challenges, where his models ranked first in several categories. He is among the top 2% of the world’s most-cited researchers, significantly advancing scientific understanding and innovation in math and AI-driven drug discovery.
Submissions are invited for original Research Articles for this collection. Articles should be submitted via the submission system, SNAPP. During the submission process, select “Machine Learning for Predictive Toxicology” from the dropdown menu.
Submitted articles will undergo BMC Pharmacology and Toxicology’s standard peer-review process and are subject to the journal’s policies. Articles will be added to the collection as they are published. The Editors handling submissions will ensure no competing interests affect the peer review process, which will be managed by another Editorial Board Member in cases where competing interests exist.
The collection is now open for submissions, with a deadline of March 14, 2025.
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