By far, the most popular and well-rounded bioinformatics platform is antiSMASH, of which substrate prediction is just one component. antiSMASH and its accessory programs (MIBiG, BiG-SCAPE, and CORASON 140 ) are the Swiss Army pocketknife of natural products bioinformatic tools, being widely used in the field and regularly maintained and updated for relevancy. The latest update to antiSMASH has incorporated an enhanced phylogenetics-based algorithm ensemble for improved NRPS A domain prediction. There is also a Hidden Markov Model (HMM)-based approach that has been incorporated, which attempts to predict proteinprotein docking regions between modules in addition to substrate specificity. The greatest strength of antiSMASH is its utilitarian ability to identify several types of biosynthetic gene clusters across a wide swath of organisms. However, this means that concise predictions for a specific type of enzyme may not always be successful, especially when similarity to known enzymes is low. There is a noticeable drop-off in prediction accuracy with antiSMASH in cases where there are fewer characterized examples, which will improve with the biochemical characterization of new clusters.