{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T11:12:34Z","timestamp":1788433954818,"version":"build-2803163510"},"reference-count":38,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>Network pharmacology has become a central paradigm in modern drug discovery, replacing the reductionist \u201cone drug, one target\u201d view with a systems-level understanding of how compounds engage networks of proteins that are linked to disease. Despite its impact, a typical network-pharmacology study remains fragmented and technically demanding: researchers must query several independent databases, install and reconcile multiple standalone tools for target collection, network construction, hub-gene ranking and pathway enrichment, and then manually bridge the results into structure-based follow-up such as molecular docking. This fragmentation is a persistent barrier, particularly for experimental and non-specialist users. Here, we present the network-pharmacology module of Drug Design Studio (DDS) 2.0, a unified, user-friendly platform that streamlines the entire workflow\u2014disease target retrieval, compound\u2013target prediction, shared-target identification, protein\u2013protein interaction (PPI) network construction, hub-gene ranking and Gene Ontology\/pathway enrichment\u2014within a single guided interface, consolidating steps that otherwise require several separate tools. Crucially, DDS 2.0 links the resulting hub genes directly to the docking and virtual-screening engine introduced in the previous DDS releases: representative experimental structures and mutant forms\u2014for instance, resistance-conferring variants found in drug-resistant strains\u2014of the target proteins are selected and streamed into a docking-ready workspace, with dedicated support for covalent binders. We validate the module against four independent published network-pharmacology studies spanning diverse diseases; DDS reproduces the reported hub genes with a mean recovery (recall) of 0.85 (range 0.80\u20130.90) and a mean Jaccard index of 0.74, and recovers the corresponding target sets and enriched pathways. DDS 2.0 thus delivers an integrated route from systems-level analysis to structure-based drug design. DDS 2.0 is freely and publicly accessible. Comprehensive user documentation is built directly into DDS and can be accessed at any time from the Documentation panel.<\/jats:p>","DOI":"10.3390\/ijms27177874","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T10:10:33Z","timestamp":1788430233000},"page":"7874","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Drug Design Studio (DDS) 2.0: A Unified Platform for Network Pharmacology Integrated with Docking and Virtual Screening Workflow for Covalent\/Non-Covalent Binders"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8711-7783","authenticated-orcid":false,"given":"Mahmoud E.","family":"Soliman","sequence":"first","affiliation":[{"name":"Molecular Bio-Computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4000, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/nrg2918","article-title":"Network Medicine: A Network-Based Approach to Human Disease","volume":"12","author":"Gulbahce","year":"2011","journal-title":"Nat. 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