Drug Design Studio (DDS) 2.0: A Unified Platform for Network Pharmacology Integrated with Docking and Virtual Screening Workflow for Covalent/Non-Covalent Binders
Sep 2026· International Journal of Molecular Sciences· Vol 27· 0 citations· 38 references
Medicine
TL;DR
The network-pharmacology module of Drug Design Studio (DDS) 2.0 is presented, a unified, user-friendly platform that streamlines the entire workflow—disease target retrieval, compound–target prediction, shared-target identification, protein–protein interaction (PPI) network construction, hub-gene ranking and Gene Ontology/pathway enrichment—within a single guided interface, consolidating steps that otherwise require several separate tools.
Abstract
Network pharmacology has become a central paradigm in modern drug discovery, replacing the reductionist “one drug, one target” 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—disease target retrieval, compound–target prediction, shared-target identification, protein–protein interaction (PPI) network construction, hub-gene ranking and Gene Ontology/pathway enrichment—within 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—for instance, resistance-conferring variants found in drug-resistant strains—of 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 0.80–0.90) 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.
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