There are many other tools that have emerged over the years to aid researchers in natural product drug discovery and dereplication of known compounds. As polyketides and nonribosomal peptides are two of the largest classes of natural products, and their respective enzymatic machinery generally follow a colinear, assembly line-like organization of active site domains, the prediction of PKS and NRPS substrates was of early intrigue. Prieto and co-workers developed the NRPS substrate predictor (NRPSsp), which uses an HMM database for its predictions. The PRISM 4 platform is a comprehensive tool for the HMM-based prediction of antibiotic structures from bacterial genomes regardless of their natural product class. Two currently active tools released in 2020 have combined random forest models and HMMs for improved predictions: AdenylPred uses machine learning to predict general adenylation preferences in all class I adenylate-forming enzymes from bacteria, fungi and plants, and SeMPI 2.0 predicts chemical structures including an emphasis on post-assembly line modifications. The latter also screens scaffolds against public gene cluster databases in an attempt to connect genomic data to compounds and thus estimate novelty. Most recently, the Mohimani group developed AdenPredictor, which uses unsupervised machine learning and the extra trees model paired with one-hot encoding features in its predictions. The authors also benchmarked existing popular in silico tools, which revealed that for substrate specificity inquiries, the most reliable models are extra trees and logistic regressions. To the best of our knowledge, the webtools listed above are the only currently active substrate prediction methods, as many more have become unavailable in recent years.