Feature Based Method for Predicting Pharmacological Interaction
Ansa Baiju1, Linda Sara Mathew2, Neethu Subash3

1Ansa Baiju*, Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India.
2Linda Sara Mathew, Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India.
3Neethu Subash, Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India. 

Manuscript received on January 06, 2021. | Revised Manuscript received on January 15, 2021. | Manuscript published on January 30, 2021. | PP: 125-129 | Volume-9 Issue-5, January 2021. | Retrieval Number: 100.1/ijrte.E5205019521 | DOI: 10.35940/ijrte.E5205.019521
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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 imbalanced, then cost-sensitive ensemble method is used to address the imbalanced problem between positive and negative samples, and learns about the minority class by assigning higher costs and optimizing their cost error. Finally, the processed data is given as input to the extreme gradient boosting classifier algorithm for predicting new drug target interaction pairs. This method can significantly improve the prediction accuracy of drug target interaction. 
Keywords: Drug Target Interaction, Lasso, Extreme gradient boosting classifier, Pseudo Position Specific Scoring Matrix.