From:  AI-driven discovery of antimicrobial peptides and derivatives: database and tools

 AMP prediction tool.

PredictorCore methodologyKey featuresReference
PyAMPArandom forestMultifunctional platform for prediction, validation, and optimization[126]
AMPlifyBiLSTM + attentionDeep learning model with balanced/imbalanced training modes[127]
TriNetTriple fusion networkSimultaneously predicts AMPs and anti-cancer peptides (ACPs)[84, 128]
Deep-AmPEP30CNNUsing pseudo-amino acids combined with CNN for AMP prediction[129]
AI4AMPLSTMAn AMP prediction tool based on physical-chemical coding[130]
PTPAMPSVMSVM performed the best and was specifically used for identifying and classifying plant-derived AMPs[87]
PeptideBERTProtein language modelA language model for fine-tuning peptides, for AMP prediction[107]
AutoPeptideMLAutomated modeling platformSupports multiple machine learning algorithms and automatically builds peptide prediction models[131]
AmPEPRFPredicting AMP based on amino acid distribution patterns[132]
HMD-AMPESM-1b; DFAccurately identify the functions of AMP and its 11 types of targets, with both robustness in small sample sizes and interpretability[106]
iAMP-CA2LMulti-scale convolution and attention mechanismExtract key features of the sequence, accurately identify and conduct interpretable analysis[26]
iAMPCNCNNA large-scale and high-quality training and testing dataset was constructed, and it demonstrated higher accuracy in most functional activity predictions[133]
PAMPredk-meansHierarchical heterogeneous integration architecture, improved particle swarm optimization (PSO) weight optimization, superior experimental performance[134]
PepGraphormerESM2; GATAn AMP prediction framework that does not require a three-dimensional structure and integrates language models with graph neural networks[108]
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AMP: antimicrobial peptide; BiLSTM: bidirectional long short-term memory; CNN: convolutional neural networks; DF: decision forest; ESM: evolutionary scale modeling; GAT: graph attention network; PSO: particle swarm optimization; RF: random forest; SVM: support vector machine.