PharmaNetViz 2.0: Enhancing Network Pharmacology

PharmaNetViz 2.0 is an advanced, interactive tool designed to overcome accessibility challenges in network pharmacology, a critical field in modern drug discovery. This updated version incorporates a graphical user interface (GUI), modular tutorials, and real-time feedback, making network pharmacology accessible to researchers at all expertise levels. The platform integrates data from external databases like STRING and KEGG, including protein-protein interactions and pathway details, enabling up-to-date biological information for analysis. Customizable pathway mapping allows researchers to focus on specific gene mutations or cellular processes, enhancing study efficiency and precision. Interactive features, such as real-time feedback, progress tracking, and guided tasks, improve user engagement and learning. PharmaNetViz 2.0 supports sustainable research by promoting computational simulations over resource-intensive laboratory experiments, thereby reducing costs, increasing efficiency, and providing reproducibility benefits that are valuable for consistency in the research process. With scalability and commercialization potential, this platform is valuable for academia, biotechnology, and pharmaceutical sectors, representing a transformative step towards accessible, impactful, and environmentally responsible drug discovery. With its scalability and commercialization potential—facilitated through subscription-based licensing and industry partnerships—PharmaNetViz 2.0 stands as a valuable tool for academia, biotechnology, and pharmaceutical sectors. By democratizing access to network pharmacology, this platform represents a transformative step towards accessible, impactful, and environmentally responsible drug discovery research.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-02-28
DOI
https://doi.org/10.5281/zenodo.14849331
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

PharmaNetViz 2.0: Enhancing Network Pharmacology

Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

PharmaNetViz 2.0: Enhancing Network Pharmacology

article en

Abstract

PharmaNetViz 2.0 is an advanced, interactive tool designed to overcome accessibility challenges in network pharmacology, a critical field in modern drug discovery. This updated version incorporates a graphical user interface (GUI), modular tutorials, and real-time feedback, making network pharmacology accessible to researchers at all expertise levels. The platform integrates data from external databases like STRING and KEGG, including protein-protein interactions and pathway details, enabling up-to-date biological information for analysis. Customizable pathway mapping allows researchers to focus on specific gene mutations or cellular processes, enhancing study efficiency and precision. Interactive features, such as real-time feedback, progress tracking, and guided tasks, improve user engagement and learning. PharmaNetViz 2.0 supports sustainable research by promoting computational simulations over resource-intensive laboratory experiments, thereby reducing costs, increasing efficiency, and providing reproducibility benefits that are valuable for consistency in the research process. With scalability and commercialization potential, this platform is valuable for academia, biotechnology, and pharmaceutical sectors, representing a transformative step towards accessible, impactful, and environmentally responsible drug discovery. With its scalability and commercialization potential—facilitated through subscription-based licensing and industry partnerships—PharmaNetViz 2.0 stands as a valuable tool for academia, biotechnology, and pharmaceutical sectors. By democratizing access to network pharmacology, this platform represents a transformative step towards accessible, impactful, and environmentally responsible drug discovery research.

Zenodo (CERN European Organization for Nuclear Research)
University of Technology Malaysia (MY), Universiti Teknologi MARA (MY)
Openalex Percentile: Top 52%
Computational Drug Discovery Methods
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PharmaNetViz 2.0: Enhancing Network Pharmacology · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS