Computational and Structural Biotechnology Journal

Scope & Guideline

Connecting Computational Excellence with Structural Innovations in Biotechnology

Introduction

Explore the comprehensive scope of Computational and Structural Biotechnology Journal through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore Computational and Structural Biotechnology Journal in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN2001-0370
PublisherELSEVIER
Support Open AccessYes
CountrySweden
TypeJournal
Convergefrom 2012 to 2024
AbbreviationCOMPUT STRUCT BIOTEC / Comp. Struct. Biotechnol. J..
Frequency-
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

Aims and Scopes

The Computational and Structural Biotechnology Journal focuses on integrating computational methods with structural biology to address complex biological questions and enhance biotechnological applications. The journal promotes innovative research that combines computational modeling, bioinformatics, and structural biology to provide insights into molecular mechanisms, drug discovery, and disease mechanisms.
  1. Computational Modeling in Biotechnology:
    Research that employs computational techniques to model biological processes, predict molecular interactions, and optimize biotechnological applications.
  2. Structural Biology Applications:
    Studies focusing on the structural characterization of biomolecules, including proteins, nucleic acids, and complexes, to understand their functions and interactions.
  3. Machine Learning and AI in Bioinformatics:
    Utilization of machine learning and artificial intelligence to analyze biological data, improve predictive models, and facilitate drug discovery processes.
  4. Omics Integration:
    Integration of various omics data (genomics, transcriptomics, proteomics, and metabolomics) to uncover complex biological networks and interactions.
  5. Microbial and Environmental Biotechnology:
    Research on microbial systems and their applications in biotechnology, including metabolic engineering and environmental sustainability.
  6. Drug Discovery and Development:
    Focused studies on the discovery and design of novel therapeutics, including the use of computational approaches to identify drug targets and design inhibitors.
  7. Systems Biology:
    Research employing systems biology approaches to understand the dynamic interactions within and between biological systems.
The journal has shown significant growth in several emerging themes, reflecting current trends in biotechnology and computational biology. These areas highlight the journal's responsiveness to advancements in technology and methodologies that are shaping the future of research.
  1. AI and Machine Learning Applications:
    A surge in research applying AI and machine learning techniques to various biological problems, including drug discovery, molecular modeling, and predictive analytics.
  2. Integration of Multi-Omics Data:
    An increasing trend in studies that integrate multi-omics data to provide comprehensive insights into biological processes and disease mechanisms.
  3. Structural Dynamics and Flexibility Studies:
    Growing interest in understanding the dynamic behavior of proteins and other biomolecules, moving beyond static structures to explore conformational changes and their functional implications.
  4. Microbiome and Host Interactions:
    A rising focus on the role of microbiomes in health and disease, exploring the complex interactions between host organisms and their microbial communities.
  5. Sustainable Biotechnology Practices:
    Emerging research on sustainable practices in biotechnology, including the use of microbial systems for bioremediation and bioenergy production.
  6. Computational Drug Repurposing:
    Increased studies on computational methods for drug repurposing, particularly in light of the COVID-19 pandemic, indicating a shift towards rapid therapeutic development.

Declining or Waning

While the Computational and Structural Biotechnology Journal continues to explore a wide range of topics, certain areas of focus appear to be declining over recent years. This may reflect shifts in research priorities or advancements in technology that allow for new methodologies and subjects of interest.
  1. Traditional Biochemical Assays:
    There is a noticeable decline in papers focusing solely on traditional biochemical assays, as the field shifts towards more integrated computational and structural approaches.
  2. Basic Laboratory Techniques:
    Research emphasizing basic laboratory techniques without computational integration is less frequent, indicating a trend towards more sophisticated, data-driven methodologies.
  3. Single-Method Studies:
    Papers focusing on single-method approaches, such as solely experimental or purely computational, are decreasing as interdisciplinary approaches become the norm.
  4. Static Structural Analysis:
    Research concentrating on static structural analysis of proteins without considering dynamic properties or interactions is waning, reflecting a growing recognition of the importance of molecular dynamics.

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