As it has become more affordable to sequence the genomes of new organisms, and more streamlined to genome mine for new biosynthetic gene clusters, several in silico tools have been developed over the last few decades for the bioinformatic prediction of A domain substrate selectivity in NRPS pathways. It should be noted that the best predictor of substrate selectivity, as discussed above, is structural data. However, after the 10AA code was established, primary amino acid sequence alignments of uncharacterized proteins with domains of known structure or selectivity became the most common method of comparison. Sequence alignments are quick, cheap, and easy to generate, but they are not always the most accurate method for substrate prediction. Caution needs to be taken as the 10AA code does not accurately apply to all enzymes and organisms. Further, when interrogating novel pathways preforming unique chemistry, there are often no homologous examples to use as a reference, making predictions tenuous at best.