Bachmann and Ravel developed a PKS/NRPS predictive BLAST server in 2009 that currently does not function. The server detected catalytic domains in PKSs and NRPSs, comparing signature motifs of A domains with those of characterized proteins and known substrates. The same year, the alignment-based tool NP.searcher was launched, which exclusively used BLAST to identify signature motifs and predict nonribosomal peptide and polyketide structures from microbial genomes. A novel in silico platform that has only recently disappeared is NRPSpredictor2, which used transductive support vector machines (TSVMs) to classify A domains according to their substrate preferences as a foundation for predictions. Another HMM-based approach was the NRPS/PKS substrate predictor tool, which may have become obsolete upon addition of HMM predictions to larger platforms like antiSMASH. One of the most accurate tools for substrate prediction was the LSI function predictor. This A domain-based algorithm used Latent Semantic Indexing (LSI) on FASTA input files and was a staple of early genome mining efforts. And finally, a relatively recent webserver developed in 2016, SEQL-NRPS has already been discontinued, but it used Sequence Learner (SEQL) as a discriminative classification method for A domains. Many of these webtools are either inaccessible from the provided URL, or else the only response that is generated is an error message. It is unclear thus far the reasons for the lack of maintenance of various in silico algorithms and webtools, but contributing factors may include funding, tedious manual curation to avoid obsolescence, and a deficiency in enthusiasm or assistance from the community. It should be noted that in recent years, the development of machine learning algorithms for genome mining has become more complex.