Advances in Computational Methods of Studying Exposure to Chemicals
A special issue of Toxics (ISSN 2305-6304). This special issue belongs to the section "Novel Methods in Toxicology Research".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 4448
Special Issue Editors
Interests: computational toxicology; machine learning methods; PBPK; QSAR; risk assessment; chemical mixtures; computational systems biology; NAMs; fate and transport modeling; statistics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The use of computational approaches has changed our understanding of toxicology by revealing important information about chemical exposure. The potential risks and effects of diverse chemical substances on human health and the environment are predicted with these methods using cutting-edge algorithms, scientific methods, technologies, and mathematical models. In this Special Issue, we explore some of the noteworthy developments and applications in computational approaches and new alternative methods that have aided our understanding of chemical exposure and risk. It will include case studies and examples of models and approaches in environmental and human health; the integration of several data streams and approaches; chemical-exposure–gene interactions; the application of NAMs for risk assessment; emerging chemicals; in silico and mixture frameworks; and research that highlights and illustrates advances in computer modeling, exposures, and chemical risk assessments.
The current information on databases, chemicals, toxicity, and multiple data streams remains insufficient to address exposure, the biomarkers of their effect, and their risks. Therefore, the aim of this Special Issue is to further contribute to the collection of information, innovative approaches, and research related to advancements in computational modeling as well as new approaches and methods that address chemical exposures and risks.
This Special Issue welcomes original articles as well as systematic reviews on these relevant topics, including but not limited to the following:
- Computational toxicology
- Machine learning
- Quantitative Structure-Activity Relationship (QSAR)
- In silico modeling
- Molecular Docking and Molecular Dynamics Simulations
- PBPK and PK modeling
- Fate and transport modeling
- High-Throughput Screening (HTS)
- Integrated Testing Strategies (ITS) Artificial intelligence
- Risk assessment and modeling
- Adverse outcome pathway (AOP)
- New approach methodologies (NAMs)
- Chemical mixtures
Dr. Patricia Ruiz
Dr. Wei-Chun Chou
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Toxics is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- computational toxicology
- machine learning
- quantitative structure-activity relationship (QSAR)
- in silico modeling
- molecular docking and molecular dynamics simulations
- PBPK and PK modeling
- fate and transport modeling
- high-throughput screening (HTS)
- integrated testing strategies (ITS)
- artificial intelligence
- risk assessment
- adverse outcome pathway (AOP)
- new approach methodologies (NAMs)
- chemical mixtures
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