## 3 Feature Encoding Methods
### Physicochemical-Based Methods  
To analyse the physicochemical characteristics of AMP sequences systematically, we implemented a suite of computational methods grounded in physicochemical property analysis. These methods enable the quantitative evaluation of critical features such as hydrophobicity, charge distribution, and secondary structure propensity, providing a robust framework for sequence-function relationship studies. **AAC**  
AAC refers to the method of calculating the frequency of each natural amino acid within a peptide sequence and arranging these values in a predefined order to generate a 20-dimensional vector, where each dimension corresponds to the frequency of a specific amino acid. **DPC**  
Dipeptide composition (DPC) refers to the method of analysing consecutive pairs of natural amino acids within a peptide sequence. It calculates the frequency of each possible dipeptide combination (20 \(\times\) 20 = 400 unique pairs) and organises these values into a 400-dimensional vector, where each dimension corresponds to the occurrence rate of a specific amino acid pair, ordered systematically (e.g., alphabetically by residue type). **TPC**  
Tripeptide composition (TPC) extends this concept to consecutive triplets of amino acids. By computing the frequency of all possible three-residue combinations (20 \(\times\) 20 \(\times\) 20 = 8000 unique triplets), an 8000-dimensional vector is generated, with each dimension representing the normalised frequency of a distinct tripeptide arrangement, similarly ordered according to a standardised convention. **GAAC**  
Grouped amino acid composition (GAAC) is a feature extraction method used to analyse peptide sequences by categorising amino acids into predefined groups on the basis of their physicochemical properties [111]. This approach simplifies the representation of a peptide sequence by focusing on the collective properties of amino acids rather than individual residues. Amino acids are classified into groups on the basis of their shared characteristics, such as hydrophobicity, charge, size, or polarity. For example, hydrophobic amino acids such as alanine (A), valine (V), leucine (L), isoleucine (I), and phenylalanine (F) might be grouped together, whereas charged amino acids such as lysine (K), arginine (R), aspartic acid (D), and glutamic acid (E) may form another group. This grouping reduces the complexity of the sequence by considering the collective properties of these groups. For each group, the GAAC algorithm calculates the frequency or proportion of amino acids within that group in the peptide sequence. For example, if a peptide sequence contains ten amino acids and four of them belong to the hydrophobic group, the composition of the hydrophobic group would be 40%. This provides a high-level view of the physicochemical properties of the peptide. **GDPC**  
Grouped dipeptide composition (GDC) is an advanced feature extraction method used in bioinformatics to analyse peptide sequences by considering the composition of dipeptides (pairs of consecutive amino acids) grouped by their physicochemical properties. This method extends the concept of grouped amino acid composition (GAAC) to dipeptides, providing a more detailed and nuanced representation of the sequence [112]. Amino acids are first grouped on the basis of their physicochemical properties, such as hydrophobicity, charge, size, or polarity. For example, hydrophobic amino acids might be grouped together, and charged amino acids might form another group. Dipeptides are then formed by considering pairs of consecutive amino acids within these groups. For example, if we have two groups, hydrophobic (H) and charged (C), the possible dipeptide groups could be HH, HC, CH, and CC. For each dipeptide group, the GDC algorithm calculates the frequency or proportion of that dipeptide in the peptide sequence. For example, if a peptide sequence contains ten dipeptides and three of them are from the hydrophobic-hydrophobic (HH) group, the composition of the HH group would be 30%.