The article introduces antiSMASH 3.0, a comprehensive resource for the genome mining of biosynthetic gene clusters (BGCs). BGCs are crucial for the discovery of novel antibiotics, chemotherapeutics, and other high-value chemicals. The new version of antiSMASH includes several significant improvements: 1. **Integration with ClusterFinder**: This algorithm probabilistically detects unknown BGCs using a hidden Markov model, enhancing the detection of novel compound families. 2. **Dereplication and Comparison with Known Pathways**: A new module allows for the comparison of identified BGCs with 1172 known BGCs, aiding in the discovery of novel compounds and providing insights into their functions. 3. **Identification and Analysis of Enzyme Active Sites**: The Active Site Finder module identifies conserved amino acid motifs, such as active sites and key residues, in biosynthetic enzymes. 4. **Improvements in Chemical Structure Prediction**: Predictions are more precise, considering the effects of ketoreductase, dehydratase, and enoylreductase on polyketide redox states. 5. **Future Developments**: The tool will integrate predicted secondary metabolite biosynthesis pathways into genome-scale metabolic models. 6. **BiosynML Output for Offline Editing**: This XML format allows for detailed analysis and manual curation of BGCs, facilitating custom bioinformatic workflows. 7. **Updated Nomenclature and Detection Logic**: The tool has been updated for polyketides, RiPPs, and other compound classes, ensuring accurate detection and classification. These enhancements make antiSMASH 3.0 a more powerful and versatile tool for the discovery and analysis of BGCs, supporting both research and metabolic engineering efforts.The article introduces antiSMASH 3.0, a comprehensive resource for the genome mining of biosynthetic gene clusters (BGCs). BGCs are crucial for the discovery of novel antibiotics, chemotherapeutics, and other high-value chemicals. The new version of antiSMASH includes several significant improvements: 1. **Integration with ClusterFinder**: This algorithm probabilistically detects unknown BGCs using a hidden Markov model, enhancing the detection of novel compound families. 2. **Dereplication and Comparison with Known Pathways**: A new module allows for the comparison of identified BGCs with 1172 known BGCs, aiding in the discovery of novel compounds and providing insights into their functions. 3. **Identification and Analysis of Enzyme Active Sites**: The Active Site Finder module identifies conserved amino acid motifs, such as active sites and key residues, in biosynthetic enzymes. 4. **Improvements in Chemical Structure Prediction**: Predictions are more precise, considering the effects of ketoreductase, dehydratase, and enoylreductase on polyketide redox states. 5. **Future Developments**: The tool will integrate predicted secondary metabolite biosynthesis pathways into genome-scale metabolic models. 6. **BiosynML Output for Offline Editing**: This XML format allows for detailed analysis and manual curation of BGCs, facilitating custom bioinformatic workflows. 7. **Updated Nomenclature and Detection Logic**: The tool has been updated for polyketides, RiPPs, and other compound classes, ensuring accurate detection and classification. These enhancements make antiSMASH 3.0 a more powerful and versatile tool for the discovery and analysis of BGCs, supporting both research and metabolic engineering efforts.