AntiSMASH 4.0 introduces significant improvements in chemistry prediction and gene cluster boundary identification. The software, originally developed in 2011, has been regularly updated to keep pace with advancements in natural product research. Version 4 enhances the identification of biosynthetic gene clusters (BGCs) by incorporating new algorithms for predicting gene cluster boundaries, such as the CASSIS algorithm for fungal genomes and the ClusterFinder algorithm for both bacterial and fungal genomes. These methods improve the accuracy of identifying where BGCs begin and end. The new version also includes improved predictions for the chemistry of non-ribosomal peptide synthetase (NRPS) adenylation domains using the SANDPUMA algorithm, which combines multiple prediction methods for better accuracy. Additionally, it provides enhanced predictions for terpene and ribosomally synthesized and post-translationally modified peptides (RiPPs), including the identification of RiPP BGCs and their structures. A domain-level alignment tool is introduced for comparative analysis of trans-AT polyketide synthase (PKS) assembly lines, aiding in the understanding of their biochemical relationships. AntiSMASH 4.0 also includes a new feature for TTA codon annotation, which is important for the expression of secondary metabolism genes in bacteria like Streptomyces. The software now supports a more user-friendly interface and improved usability features, including an updated ClusterBlast database and REST-like web API for integration with other tools. The software is freely accessible via the antiSMASH website and is available as an open-source tool. It continues to be a valuable resource for researchers in the field of natural product discovery, facilitating the identification and characterization of biosynthetic gene clusters in bacterial and fungal genomes. The improvements in antiSMASH 4.0 ensure that it remains up-to-date with the latest developments in natural product research and will further aid in the discovery of novel bioactive molecules.AntiSMASH 4.0 introduces significant improvements in chemistry prediction and gene cluster boundary identification. The software, originally developed in 2011, has been regularly updated to keep pace with advancements in natural product research. Version 4 enhances the identification of biosynthetic gene clusters (BGCs) by incorporating new algorithms for predicting gene cluster boundaries, such as the CASSIS algorithm for fungal genomes and the ClusterFinder algorithm for both bacterial and fungal genomes. These methods improve the accuracy of identifying where BGCs begin and end. The new version also includes improved predictions for the chemistry of non-ribosomal peptide synthetase (NRPS) adenylation domains using the SANDPUMA algorithm, which combines multiple prediction methods for better accuracy. Additionally, it provides enhanced predictions for terpene and ribosomally synthesized and post-translationally modified peptides (RiPPs), including the identification of RiPP BGCs and their structures. A domain-level alignment tool is introduced for comparative analysis of trans-AT polyketide synthase (PKS) assembly lines, aiding in the understanding of their biochemical relationships. AntiSMASH 4.0 also includes a new feature for TTA codon annotation, which is important for the expression of secondary metabolism genes in bacteria like Streptomyces. The software now supports a more user-friendly interface and improved usability features, including an updated ClusterBlast database and REST-like web API for integration with other tools. The software is freely accessible via the antiSMASH website and is available as an open-source tool. It continues to be a valuable resource for researchers in the field of natural product discovery, facilitating the identification and characterization of biosynthetic gene clusters in bacterial and fungal genomes. The improvements in antiSMASH 4.0 ensure that it remains up-to-date with the latest developments in natural product research and will further aid in the discovery of novel bioactive molecules.