Current Computer-aided Drug Design - Impact Factor

Current Computer-aided Drug Design - Bentham Science Publishers | 2024 Impact Factor:1.6 | Cite Score:3.0 | Q3

Current Computer-aided Drug Design Journal - Bentham Science Publishers

Impact Factor and Journal Rank of Current Computer-aided Drug Design

About: Current Computer-Aided Drug Design is dedicated to publishing the latest developments in drug design that utilize computational methodologies. The journal serves as a crucial resource for medicinal chemists seeking to stay informed and updated on the cutting-edge advancements in this field.
Objectives
The primary objectives of the journal include:
Advancing Drug Discovery: To publish research that enhances the efficiency and effectiveness of drug discovery through computational techniques.
Methodological Innovations: To showcase new computational methodologies and algorithms used in drug design.
Application in Drug Development: To explore how computational methods are applied across various stages of drug development, from target identification to clinical trials.
Integration of Disciplines: To foster interdisciplinary research between medicinal chemistry, computational biology, pharmacology, and related fields.
Impact on Healthcare: To highlight the impact of computer-aided drug design on improving treatment options and patient outcomes.
Key Focus Areas
The journal covers a range of topics related to computer-aided drug design, including:
Molecular Docking: Techniques and algorithms for predicting the binding affinity of small molecules to target proteins.
Quantitative Structure-Activity Relationships (QSAR): Methods for predicting and optimizing the biological activity of compounds.
Virtual Screening: High-throughput screening methods using computational approaches to identify potential drug candidates.
Structure-Based Drug Design: Rational drug design based on the three-dimensional structure of biological targets.
Ligand-Based Drug Design: Design strategies based on the properties and interactions of known ligands.
Drug Repurposing: Computational approaches to identify new therapeutic uses for existing drugs.
Bioinformatics Tools: Use of computational tools and databases for drug design and discovery.
Impact and Significance
Current Computer-Aided Drug Design plays a critical role in:
Advancing Scientific Knowledge: By publishing original research that expands the understanding of computational techniques in drug design.
Accelerating Drug Development: By facilitating faster and more cost-effective drug discovery processes.
Educational Resource: By providing insights and methodologies that support the training of future researchers and practitioners in medicinal chemistry and computational biology.
Industry Application: By influencing pharmaceutical industry practices and strategies in drug discovery and development.
Global Collaboration: By fostering international collaboration and knowledge exchange among researchers and practitioners in the field.

Journal Home: Journal Homepage

Editor-in-Chief: Dong-Qing Wei

Scope: Current Computer-Aided Drug Design aims to be a leading journal for medicinal chemists, focusing on the latest developments in drug design using computational techniques. It serves as a vital resource for staying informed about advancements in computer-aided methodologies and their applications in drug discovery. The journal covers a broad range of topics within computer-aided drug design, including:
Computational Techniques: Innovations and advancements in computational methods and algorithms for drug design and discovery.
Molecular Modeling: Applications of molecular modeling, docking, and simulation techniques in predicting and optimizing drug-target interactions.
Structure-Based Drug Design: Strategies and methodologies for designing drugs based on the three-dimensional structure of biological targets.
Ligand-Based Drug Design: Methods for designing drugs based on the properties and structures of known ligands.
Virtual Screening: High-throughput computational screening of chemical libraries to identify potential drug candidates.
Quantitative Structure-Activity Relationship (QSAR): Development and application of QSAR models in rational drug design.
Bioinformatics and Cheminformatics: Use of computational tools and databases in analyzing biological and chemical data relevant to drug discovery.
Drug Repurposing: Computational approaches for identifying new therapeutic uses for existing drugs.