| ACPs | Anticancer peptides | |
| non-ACPs | Non-Anticancer peptides | |
| AAC | amino acid composition | |
| DPC | dipeptide composition | |
| BPF | binary profiles feature | |
| PAAC | pseudo-amino acid composition | |
| g-gap DPC | g-gap dipeptide composition | |
| RBF | radial basis function | |
| ATC | atomic composition | |
| RAAAC | reduced amino acid alphabet composition | |
| CTD | composition-transition-distribution | |
| QSO | quasi-sequence-order | |
| AAIF | amino acid index | |
| GAAC | grouped amino acid composition | |
| Am-PAAC | amphiphilic pseudo amino acid composition | |
| OPF | overlap property feature | |
| TOBF | twenty-one-bit feature | |
| AKDC | daptive skip dipeptide composition | |
| CNN | convolutional neural networks | |
| GCN | graph convolutional networks | |
| SVM | support vector machine | |
| RF | Random Forest | |
| PNN | probability neural network | |
| GRNN | generalized regression neural network | |
| KNN | k-nearest neighbors | |
| RNN | recurrent neural network | |
| LSTM | long short-term memory | |
| CKSAAGP | k-spaced amino acid group pairs | |
| Bi-LSTM | bidirectional long short-term memory network | |
| NLP | natural language processing | |
| SMILES | Simplified Molecular Input Line Entry System | |
| BERT | bidirectional encoder representation transformer | |
| RoBerta | robustly optimized BERT pre-training approach | |
| BPE | byte pair encoding | |
| ChemBerta + ST | ChemBERTa with a SMILES tokenizer | |
| ChemBerta + BT | ChemBERTa with a BPE’s tokenizer | |
| TP | True Positive | |
| FP | False Positive | |
| TN | True Negative | |
| FN | False Negative | |
| ACC | accuracy | |
| MCC | Matthews correlation | |
| SE | sensitivity | |
| SP | specificity | |
| AUC | area under curve | |
| ROC | receiver operating characteristic |