To deal with this model, the sequence-similarity-searchbased tools, such as BLAST [49] , are usually used to find the desired results. Unfortunately, this kind of approach failed to work when the query protein did not have significant homology to the proteins in the training dataset. Thus, various non-sequential representations or discrete models were proposed. The simplest discrete model was based on the amino acid composition (AAC) (see, e.g., [50] ). However, if using the AAC model to represent a protein, all its sequence-order information will be lost. To avoid completely losing the sequence-order information, the pseudo amino acid composition (Pse-AAC) was proposed [36] to represent the sample of a protein. The PseAAC can be used to represent a protein sequence with a discrete model yet without completely losing its sequence-order information. For further information about PseAAC, see the web-page by clicking the link http://en. wikipedia.org/wiki/Pseudo_amino_acid_composition. Ever since the concept of PseAAC was introduced, it has been widely used to study various problems in proteins and protein-related systems (see, e.g., [37, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66] ). Meanwhile, many different forms of discrete models were also proposed (see, e.g., [20, 30, 32, 51, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82] ). However, regardless of how much different these models are, they just belong to different forms of PseAAC, as elucidated in a recent comprehensive review [83] . Here, we are to propose a different PseAAC to represent drug-targeted proteins in terms of their biochemical and physicochemical features [84] . Six different types of features were considered: (1) hydrophobicity, (2) polarizability, (3) polarity, (4) secondary structure, (5) normalized van der Waals volume, and (6) solvent accessibility.