Classical sequence alignment algorithms, such as the Smith-Waterman and Needleman-Wunsch 31 algorithms, are computationally intensive and do not scale well to large problems. Many papers have advocated the use of alignment-free methods to determine sequence similarity . The success of these endeavours notwithstanding, sequence alignment functions are effective notions of biological-sequence similarity that can reflect ancestral, structural or functional similarity and therefore should not be overlooked. Several studies have utilised sequence alignment functions for AMP prediction. For example, Wang et al. and Ng et al. utilised the BLAST algorithm in a classification model. Whilst these approaches led to accurate models, the BLAST algorithm is a heuristic method that finds only approximate optimal alignments. Hence, it is interesting to consider whether the same approaches using the optimal alignment score would improve the models.