AASDTool is to generate the peptide feature vectors in batches, which contains 20 structural characterization scales. By uploading txt text containing peptide sequences, you can quickly generate the feature vectors of bioactive peptides, which can be used for molecular modeling and bioinformatics prediction, etc.

Descriptors  Explanation 
Through factor analysis, the FASGAI descriptors cluster 334 physicochemical properties of each of 20 coded amino acids into 6 factors, which are related to hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, respectively.  
The 3 principal component scores are derived from PCA of a matrix of 29 physicochemical variables for 20 coded amino acid are related to hydrophilicity (z_{1}), bulk (z_{2}), and electronic properties (z_{3}).  
The NNAAIndex scales characterize a total of 155 physiochemical properties of 22 natural and 593 nonnatural amino acids, followed by clustering the structural matrix into 6 representative property patterns by factor analysis. The six factors are geometric characteristics, Hbond, connectivity, accessible surface area, integy moments index, and volume and shape, respectively.  
The isotropic surface area (ISA) approximates the hydrophobic character of the side chain substituent; The electronic charge index (ECI) is a measure of the charge concentration of the amino acid. Each residue was described by a combination of ISA and ECI descriptors. 

MSWHIM indexes, which are three principal components derived from PCA, are a collection of 36 statistical indexes aimed at extracting and condensing steric and electrostatic 3Dproperties of a molecule.  
The SZOTT descriptors are derived from PCA of a matrix of 1369 structural variables including 0D, 1D, 2D and 3D information for 20 coded amino acids.  
The 8 principal component scores of STscales are derived from 827 structural variables of 167 amino acids by PCA.  
The 5 principal component scores of Tscales are derived from PCA on the collected 67 kinds of structural and topological variables of 135 amino acids.  
The VHSE descriptors are derived from the PCA on independent families of 18 hydrophobic properties, 17 steric properties, and 15 electronic properties, respectively. Among them, VHSE_{1} and VHSE_{2} are related to hydrophobic properties, VHSE_{3} and VHSE_{4} to steric properties, and VHSE_{5}–VHSE_{8} to electronic properties.  
The 3 principal component scores as VSTV descriptors are derived from PCA on a matrix of 25 structural and topological variables of 20 coded amino acids.  
The 7 principal properties of the GRID scales are derived from PCA on interaction energies of 20 coded amino acids, with six different probes mimicking various functional groups which can be involved in peptidepeptide interactions.  
The DPPS descriptors for 20 amino acids are derived by PCA. The electronic properties of the amino acid are characterized by V_{1}V_{4}, steric properties by V_{5} and V_{6}, hydrophobic properties by V_{7} and V_{8}, and hydrogen bond contributions by V_{9} and V_{10}.  
The BLOSUM matrixderived descriptors (BLOSUM) including 10 indices, representing hydrophobicity, alphahelix propensity, betasheet propensity, bulkiness, charge, and composition, are based on both physicochemical properties that have been subjected to a ARIMAX analyses and an alignment matrix of 20 coded amino acids, the BLOSUM62 matrix.  
These 12 vectors of HESH for 20 amino acids were obtained by PCA. The V_{1}–V_{4} were hydrophobic properties, V_{5}–V_{6} were steric properties, V_{7}–V_{10} were electronic properties, and V_{11}–V_{12} were hydrogen bond contribution properties.  
Three kinds of physicochemical parameters selected from properties of 20 coded amino acids, namely Van Der Waal’s volume, net charge index and hydrophobic parameter, construct the vectors to characterize the structures of peptides.  
The 8 principal component scores based on a PCA analysis of 58 amino acids properties explain 92% of the variances. For these predominant components, the first one is related to hydrophobicity and the second one is related to the size of amino acids.  
The amino acids indices of QTMS are obtained by application of PCA on the unfolded loadings of the a data matrix of QTMS of all bonds of amino acids.  
The 16 principal component scores are derived from PCA of 150 radial distribution function and 74 geometrical descriptors.  
The 10 principal component scores are obtained by PCA on 99 WHIM and 197 GETWAY descriptors.  
The 9 principal component scores are obtained by PCA on 99 WHIM descriptors. 