Journal of Chemical Information and Modeling

Scope & Guideline

Innovating Chemical Engineering Through Information Modeling.

Introduction

Immerse yourself in the scholarly insights of Journal of Chemical Information and Modeling with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN1549-9596
PublisherAMER CHEMICAL SOC
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2005 to 2024
AbbreviationJ CHEM INF MODEL / J. Chem Inf. Model.
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address1155 16TH ST, NW, WASHINGTON, DC 20036

Aims and Scopes

The Journal of Chemical Information and Modeling primarily focuses on the intersection of chemistry and computational modeling, emphasizing innovative methodologies and applications in chemical informatics. The journal aims to present cutting-edge research that utilizes computational techniques to enhance our understanding of molecular systems, facilitate drug discovery, and advance material science.
  1. Computational Drug Discovery:
    Research often involves the application of computational techniques in drug discovery processes, including virtual screening, molecular docking, and structure-based design of inhibitors.
  2. Molecular Dynamics and Simulations:
    A significant focus on molecular dynamics simulations to explore the behavior of biomolecules, understand interactions, and study conformational changes.
  3. Machine Learning in Chemistry:
    Integration of machine learning approaches for predicting molecular properties, enhancing computational efficiency, and improving the accuracy of chemical informatics.
  4. Chemical Informatics and Data Mining:
    Utilization of data mining techniques to extract meaningful information from chemical databases, facilitating the design and optimization of chemical compounds.
  5. Structure-Activity Relationship (SAR) Studies:
    Investigations into the correlations between chemical structure and biological activity, often leading to insights that guide the design of new therapeutic agents.
  6. Multiscale Modeling:
    Research that combines various modeling techniques, from quantum mechanics to coarse-grained approaches, to understand complex chemical and biological systems.
Recent publications in the Journal of Chemical Information and Modeling highlight several emerging trends and themes that reflect the evolving landscape of chemical research. These trends underscore the journal's commitment to advancing knowledge through innovative methodologies and interdisciplinary collaboration.
  1. AI and Machine Learning Integration:
    A significant trend is the increasing integration of artificial intelligence and machine learning techniques in various aspects of chemistry, including drug discovery, molecular property predictions, and chemical synthesis optimization.
  2. Enhanced Drug-Target Interaction Predictions:
    Emerging methodologies focus on accurately predicting drug-target interactions, with an emphasis on developing models that consider both structural and dynamic aspects of proteins and ligands.
  3. Multiscale and Hybrid Modeling Approaches:
    There is a growing interest in multiscale modeling that combines quantum mechanical, molecular mechanical, and coarse-grained approaches to study complex biochemical systems.
  4. Focus on Biologics and Biopharmaceuticals:
    Increasing attention is being paid to biologics, including the computational modeling of protein-protein interactions, antibody design, and the development of new therapeutic peptides.
  5. Sustainable Chemistry and Green Methods:
    Research initiatives are increasingly focusing on sustainable chemistry, aiming to develop eco-friendly methodologies and materials, as well as exploring the environmental impacts of chemical processes.

Declining or Waning

While the Journal of Chemical Information and Modeling continues to thrive in various areas, some themes have shown a decline in prominence over recent years. These waning scopes indicate shifts in research focus and emerging methodologies.
  1. Traditional QSAR Models:
    The reliance on traditional quantitative structure-activity relationship (QSAR) models has decreased as machine learning approaches gain traction, leading to a decline in publications centered solely on classical QSAR methodologies.
  2. Biophysical Studies without Computational Insights:
    Papers focusing exclusively on experimental biophysical studies without integrating computational modeling or simulations are becoming less common, as the field increasingly values interdisciplinary approaches.
  3. Static Structural Analysis:
    Research emphasizing static structural analysis of compounds, without consideration for dynamic behavior or interactions, is waning as the community shifts towards understanding molecular flexibility and dynamics.
  4. Single-Method Approaches:
    There is a noticeable decline in studies that apply only one computational methodology, as the field increasingly adopts hybrid approaches that combine multiple techniques for more comprehensive insights.

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