From Figure and Table , it is noticed that in most trials, validation loss is consistently The final settings for the transformer were chosen from the lowest validation KL divergence loss after 1,000 or 2,000 epochs, where some trials were early terminated to save computational costs when training and validation losses become stable. Dimension of embedding for the indices was 128. Six encoder and decoder units were assembled. In each encoder and decoder unit, the number of multi-head attention subunits was four, and there are four hidden layers and the dimension of the feed forward unit in each layer was 128 with dropout rate of 0.1. Adam optimiser was used to update model parameters with a varied learning rate introduced from Vaswani et al. The batch size was 16 The predicted amino acid sequences are compared with the predicted amino acid sequence of the input enzyme by Levenshtein distance. The algorithm of Levenshtein distance is described in Eq. 9, which determines the minimum steps of edits between two strings, and by edits, it means the insertion, deletion or substitution of a single character in a string. In Eq. 9,