Computational Toxicology - Elsevier | 2024 Impact Factor: 2.9 | Cite Score:6.6 | Q2

Cite Score and Journal Rank of Computational Toxicology

• About: The Computational Toxicology Journal is a peer-reviewed publication dedicated to the use of computational methods and tools in the field of toxicology. It serves as a platform for researchers, toxicologists, and data scientists to publish original research articles, reviews, and technical papers on the development and application of computational techniques in understanding and assessing toxicological risks.
• Objective:
  The primary objective of the journal is to advance the understanding and application of computational approaches in toxicology. It aims to facilitate the exchange of knowledge and ideas among experts working on innovative computational models, simulations, and data analysis methods that improve the assessment of chemical safety and toxicity.
• Topics Covered:
  The Computational Toxicology Journal covers a wide range of topics including: Development and validation of computational toxicology models Quantitative structure-activity relationships (QSAR) Computational approaches for risk assessment Toxicogenomics and data mining Integration of omics data in toxicology Application of machine learning and artificial intelligence in toxicology
• Impact:
  The journal significantly impacts both academia and industry by promoting research and development in computational toxicology. Its publications contribute to advancements in understanding chemical safety and toxicity through innovative computational methods, helping to enhance regulatory decision-making and risk management practices.
• Significance:
  The Computational Toxicology Journal is significant in advancing the field of toxicology by promoting high-quality, peer-reviewed research in computational methods. By providing a forum for the latest advancements and applications in computational toxicology, the journal supports the development of more accurate and efficient tools for assessing chemical risks and ensuring public health and safety.

• Journal Home:  Journal Homepage

• Editor-in-Chief:  Professor Mark Cronin

• Scope: The Computational Toxicology journal focuses on the use of computational methods and techniques to understand and predict the effects of chemicals on biological systems. Here are the key areas typically covered in this journal:
• 1. Predictive Toxicology Models
  Development and validation of computational models for predicting chemical toxicity
  Use of quantitative structure-activity relationship (QSAR) models
  Machine learning and artificial intelligence approaches for toxicity prediction
• 2. In Silico Toxicology
  Techniques for simulating and modeling toxicological processes
  Integration of in silico methods with experimental data
  Applications of in silico methods in risk assessment and regulatory decision-making
• 3. Bioinformatics and Toxicogenomics
  Analysis of genomic and proteomic data related to chemical exposure
  Bioinformatics tools and methods for toxicological research
  Toxicogenomic approaches to understanding chemical-induced biological responses
• 4. Computational Systems Toxicology
  Modeling of biological systems and pathways affected by chemicals
  Integration of omics data with systems biology approaches
  Simulation of chemical interactions with cellular and molecular targets
• 5. Adverse Outcome Pathways (AOPs)
  Development and application of AOP frameworks in toxicology
  Computational approaches for mapping and modeling AOPs
  Use of AOPs in risk assessment and regulatory science
• 6. Chemoinformatics and Data Mining
  Techniques for mining and analyzing chemical and toxicological data
  Applications of chemoinformatics in identifying toxicological patterns
  Data-driven approaches for chemical hazard assessment
• 7. Environmental Toxicology and Risk Assessment
  Computational methods for assessing environmental toxicity and exposure
  Modeling of chemical fate and transport in the environment
  Risk assessment approaches using computational tools
• 8. Computational Approaches to Drug Toxicity
  Prediction of drug-induced toxicity and adverse drug reactions
  Integration of computational methods with clinical and preclinical data
  Tools for assessing drug safety and efficacy
• 9. Computational Methods for Regulatory Science
  Application of computational toxicology in regulatory settings
  Development of guidelines and standards for computational methods
  Case studies of regulatory applications of computational toxicology
• 10. Computational Toxicology and Personalized Medicine
  Use of computational methods to tailor toxicity assessments to individual characteristics
  Applications of personalized medicine approaches in toxicology
  Integration of genetic and environmental factors in toxicity prediction
• Latest Research Topics for PhD in Computer Science

• Print ISSN:  2468-1113

  Electronic ISSN:  

• Abstracting and Indexing:  Scopus

• Imapct Factor 2024:  2.9

• Subject Area and Category:   Computer Science, Computer Science Applications, Environmental Science, Health, Toxicology and Mutagenesis, Pharmacology, Toxicology and Pharmaceutics, Toxicology

• Publication Frequency:  

• H Index:  28

• Best Quartile:

  Q1:  

  Q2:  Computer Science Applications

  Q3:  

  Q4:  

• Cite Score:  6.6

• SNIP:  0.888

• Journal Rank(SJR):  0.744

• Latest Articles:   Latest Articles in Computational Toxicology

• Guidelines for Authors: Computational Toxicology Author Guidelines

• Paper Submissions: Paper Submissions in Computational Toxicology

• Publisher:  Elsevier BV

  Country:  Netherlands

• Paper Writing Services - S-Logix
• Paper Publication Support - S-Logix