Prediction of drug target interaction is an extrusive domain of drug discovery and repositioning of drugs. Most conventional studies are carried out in early years in the wet laboratory, but it is very expensive and time consuming. So nowadays, the use of machine learning techniques to predict drug target pairs. A new method of interaction targeting drugs is introduced in this paper. Use the Pseudo Position Specific Scoring Matrix (PsePSSM) is used to represent the target, which generate features that describe the original information of protein. The drug chemical structure information can be extracted through FP2 molecular fingerprint which describe the molecular structure information. Then a drug target interaction network is constructed using bipartite graph where in which each node represents a target or drug and each link indicates a drug target interaction. From the above stages, the data contains some noise and redundant data which have a negative impact on the prediction output. So, LASSO (Least Absolute Shrinkage and Selection Operator) method is handle it and reduce the dimension of the extracted feature information of original data. But drug target pair samples have some
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