Table 2.

A comprehensive summary of the implemented architectures for AMP prediction

Type	Architecture	Year	Feature space size	Feature extraction	Feature selection	Algorithm	Reference
Deep learning-based architectures	AMPScanner V2	2018	128	Embedding	none	DNN with convolutional, maximal pooling and LSTM layers	[46]
	Deep-AmPEP30	2020	86	PseKRAAC	none	CNN with two convolutional layers, two maximum pooling layers, and one fully connected hidden layer	[49]
Non-deep learning-based architectures	CS-AMPpred	2012	9	-helix, -helix propensity, -sheet, loop formation, charge, hydrophobicity, flexibility, amphipathicity, hydrophobic moment	PCA	SVM	[42]
	iAMP-2L	2013	40	PseAAC	none	FKNN	[43]
	SVM-LZ	2015	1000	LZ complexity pairwise similarity scores	none	sequence alignment (blastp), SVM	[50]
	MLAMP	2016	30	PseAAC with grey model coefficients	none	RF	[51]
	AmPEP	2018	23	Selected distribution descriptors	none	RF	[52]
	AMAP	2019	20	AAC	none	SVM	[44]
	AmPEPpy	2020	105	CTD	none	RF	[53]
	MACREL	2020	22	relative position of the first occurrence of residues in three groups of amino acids defined by their free energy of transition in a peptide from a random coil in aqueous environment to an organized helical structure in a lipid phase, solvent accessibility, AAC, charge and solubility, instability, aliphaticity, propensity to bind to membranes, hydrophobicity	none	RF	[54]
	AmpGram	2020	32 835–33 612	n-grams	QuiPT	RF	[18]
	ampir	2020	27	PseAAC, amphiphilicity, hydrophobicity, isoelectric point, molecular weight, net charge	none	SVM	[24]