STITCH 5 is a database that integrates protein-chemical interaction data from various sources into a single, user-friendly resource. It includes over 430,000 chemicals and more than 9.6 million proteins from 2031 eukaryotic and prokaryotic genomes. The database provides a global network view of interactions, allowing users to visualize binding affinities between chemicals and proteins. A new feature enables filtering of interactions based on tissue-specific expression, enhancing the relevance of interactions to specific tissues. The database combines data from high-throughput experiments, manually curated datasets, and prediction methods to create a comprehensive network of protein-chemical interactions. It also incorporates text mining and structure-based prediction methods to identify interactions. The database includes confidence scores for each interaction, based on the strength of the evidence. The new version of STITCH includes a network view that displays binding affinities, with edge thickness reflecting the strength of the interaction. Users can filter interactions based on tissue-specific expression, using data from TISSUES and the Expression Atlas. This allows for more accurate identification of interactions relevant to specific tissues. STITCH is widely used for various applications, including identifying potential drug targets, analyzing drug combinations, and developing new web-based resources. It is also used as a benchmark for predicting drug-protein interactions and side effects. The database is accessible via a redesigned web interface, an API, and downloadable files for large-scale analysis. The integration of tissue-specific data enhances the utility of the database for drug discovery and development.STITCH 5 is a database that integrates protein-chemical interaction data from various sources into a single, user-friendly resource. It includes over 430,000 chemicals and more than 9.6 million proteins from 2031 eukaryotic and prokaryotic genomes. The database provides a global network view of interactions, allowing users to visualize binding affinities between chemicals and proteins. A new feature enables filtering of interactions based on tissue-specific expression, enhancing the relevance of interactions to specific tissues. The database combines data from high-throughput experiments, manually curated datasets, and prediction methods to create a comprehensive network of protein-chemical interactions. It also incorporates text mining and structure-based prediction methods to identify interactions. The database includes confidence scores for each interaction, based on the strength of the evidence. The new version of STITCH includes a network view that displays binding affinities, with edge thickness reflecting the strength of the interaction. Users can filter interactions based on tissue-specific expression, using data from TISSUES and the Expression Atlas. This allows for more accurate identification of interactions relevant to specific tissues. STITCH is widely used for various applications, including identifying potential drug targets, analyzing drug combinations, and developing new web-based resources. It is also used as a benchmark for predicting drug-protein interactions and side effects. The database is accessible via a redesigned web interface, an API, and downloadable files for large-scale analysis. The integration of tissue-specific data enhances the utility of the database for drug discovery and development.