This KNIME workflow facilitates comprehensive assessment of biological target tractability by integrating up-to-date data from multiple public resources including UniProt, ChEMBL, PDB, Open Targets, and Human Protein Atlas. Users can input target lists using UniProt accession codes or gene symbols, either via file upload or direct entry.
The workflow retrieves extensive functional and expression information such as protein function, subcellular localization, tissue-specific and disease-relevant expression data, and related disease annotations. This enables evaluation of target relevance in the biological context of interest.
For tractability insights, it collects rich druggability data comprising associated mechanisms of action (MOA), linked approved and investigational drugs with detailed activity profiles, and chemical probes validated for target modulation. It further aggregates compound bioactivity data by assay type, supporting ligand-based drug discovery and QSAR modeling.
Structure-based tractability is informed through retrieval of experimental 3D structures and ligand information from PDB. Additional small-molecule tractability scores and druggable family classifications come from Open Targets, delivering a multi-dimensional view of target feasibility.
Interactive visualizations summarize these data, enabling filtering and detailed exploration of targets’ clinical development status, drug-target interactions, chemical probe availability, compound activity breadth, and structural data quality. The workflow supports export of results for downstream analysis, making it a valuable tool for prioritizing targets to guide resource allocation in drug discovery projects.
The workflow retrieves extensive functional and expression information such as protein function, subcellular localization, tissue-specific and disease-relevant expression data, and related disease annotations. This enables evaluation of target relevance in the biological context of interest.
For tractability insights, it collects rich druggability data comprising associated mechanisms of action (MOA), linked approved and investigational drugs with detailed activity profiles, and chemical probes validated for target modulation. It further aggregates compound bioactivity data by assay type, supporting ligand-based drug discovery and QSAR modeling.
Structure-based tractability is informed through retrieval of experimental 3D structures and ligand information from PDB. Additional small-molecule tractability scores and druggable family classifications come from Open Targets, delivering a multi-dimensional view of target feasibility.
Interactive visualizations summarize these data, enabling filtering and detailed exploration of targets’ clinical development status, drug-target interactions, chemical probe availability, compound activity breadth, and structural data quality. The workflow supports export of results for downstream analysis, making it a valuable tool for prioritizing targets to guide resource allocation in drug discovery projects.