Molecular Informatics
Scope & Guideline
Empowering Interdisciplinary Collaboration in Molecular Informatics
Introduction
Aims and Scopes
- Computational Drug Discovery:
The journal extensively covers methodologies related to the computational identification and optimization of drug candidates, including virtual screening, molecular docking, and pharmacophore modeling. - Cheminformatics and Data Science:
Research in this area focuses on the application of data science techniques to chemical data, including the development of predictive models using machine learning and statistical methods. - Molecular Modeling and Simulation:
The journal publishes studies employing molecular dynamics simulations, quantum chemical calculations, and other modeling techniques to understand molecular interactions and dynamics. - Toxicology and ADMET Studies:
There is a strong emphasis on the prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of compounds, which is crucial for drug development. - Natural Product Informatics:
Research highlighting the role of natural products in drug discovery and their computational analysis is a significant aspect, including the exploration of chemical space related to natural compounds.
Trending and Emerging
- Machine Learning and AI Applications:
There is a significant rise in the use of artificial intelligence and machine learning techniques for predictive modeling, data analysis, and drug discovery processes, showcasing the integration of advanced computational methods. - Integrated Approaches for Drug Repurposing:
Emerging studies are increasingly focusing on drug repurposing strategies, utilizing network-based approaches and computational tools to identify new uses for existing drugs, particularly in response to global health challenges. - Interdisciplinary Research:
Research that combines insights from various fields such as bioinformatics, chemoinformatics, and systems biology is gaining traction, promoting a holistic view of molecular interactions and drug development. - Natural Product-Based Drug Discovery:
There is a resurgence of interest in natural products, with studies exploring their potential as sources for new drug candidates through computational methodologies, reflecting a trend towards sustainable and bio-inspired drug design.
Declining or Waning
- Traditional QSAR Models:
While quantitative structure-activity relationship (QSAR) models remain important, there is a noticeable decline in traditional approaches, with a shift towards more advanced machine learning techniques that enhance predictive capabilities. - Basic Molecular Docking Studies:
Research focused solely on molecular docking without integration of other computational methods (like dynamics simulations or machine learning) is becoming less frequent, as more comprehensive approaches are favored. - Single-Target Drug Discovery:
The trend is moving towards multi-target and polypharmacology strategies, leading to a decrease in studies that focus exclusively on single-target drug discovery approaches.
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