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Call for papers - Machine learning for predictive toxicology

Guest Editors

Duc Nguyen, PhD, University of Tennessee, Knoxville, USA
Marcus Tullius Scotti, PhD, Federal University of Paraíba, Brazil

Submission Status: Open   |   Submission Deadline: 14 June 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.

Meet the Guest Editors

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Duc Nguyen, PhD, University of Tennessee, Knoxville, 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 Tennessee, Knoxville. 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.

Marcus Tullius Scotti, PhD, Federal University of Paraíba, Brazil

Dr Marcus Tullius Scotti graduated in Chemical Engineering from the Polytechnic School of the University of São Paulo in 1999. He worked in a Brazilian electronics and telecommunications services company called Gradiente (1999-2003) and had specialization in industrial administration at the University of São Paulo (2001-2003), Masters (2005), and PhD (2008) degree in Organic Chemistry at the Chemistry Institute of the University of São Paulo. Dr Scotti started to work as a Professor of Organic Chemistry at the Federal University of Paraíba (Federal University of Paraíba) in 2009 and is currently an Associate Professor. He holds a Post-doc in cheminformatics at Nova University Lisbon (2013). His research interests include cheminformatics approaches, QSAR, web tools, and databases.

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


Image credit: © Andrey Popov / stock.adobe.com

  1. Thyroid eye disease (TED) is an autoimmune inflammatory disorder of the orbit, associated with a range of potential clinical sequelae. Tumor cells in TED overexpress pro-angiogenic factors, driving the formati...

    Authors: Zixuan Wu, Jun Peng, Xi Long, Kang Tan, Xiaolei Yao and Qinghua Peng
    Citation: BMC Pharmacology and Toxicology 2025 26:53
    Content type: Research Published on:

  2. Chronic obstructive pulmonary disease (COPD) pathogenesis is influenced by environmental factors, including Benzo(a)pyrene (BaP) exposure. This study aims to identify BaP-related toxicological targets and eluc...

    Authors: Jiehua Deng, Lixia Wei, Yongyu Chen, Xiaofeng Li, Hui Zhang, Xuan Wei, Xin Feng, Xue Qiu, Bin Liang and Jianquan Zhang
    Citation: BMC Pharmacology and Toxicology 2025 26:33
    Content type: Research Published on:

  3. Osteoporosis (OP), often termed the “silent epidemic,” poses a substantial public health burden. Emerging insights into the molecular functions of FBXW4 have spurred interest in its potential roles across vari...

    Authors: Jinxiao Li, Jing Li, Man Zheng, Jinxing Liu and Xinyou Zhao
    Citation: BMC Pharmacology and Toxicology 2025 26:20
    Content type: Research Published on:

  4. Alzheimer’s disease (AD), a hallmark of age-related cognitive decline, is defined by its unique neuropathology. Metabolic dysregulation, particularly involving glutamine (Gln) metabolism, has emerged as a crit...

    Authors: Naifei Xing, Jingwei Yan, Rong Gao, Aihua Zhang, Huiyan He, Man Zheng and Guojing Li
    Citation: BMC Pharmacology and Toxicology 2025 26:19
    Content type: Research Published on:

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.