• M.ARUN KUMAR
• DR.T.S.BASKARAN

Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Assess that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our aim is to focus on machine learning approaches and provide a comprehensive overview. First, to summarize a brief list of databases frequently used in drug discovery. Next, to adopt a hierarchical classification scheme and propose several representative methods of each category, especially the recent state-of-the-art methods. In addition, to compare the advantages and limitations of methods in each category. The remaining challenges and future outlook of machine learning in drug-target interaction prediction

Drugs, Drug effects, Machine Learning models, Predication Algorithm

"PREDICTION OF DRUG-DRUG INTERACTIONS (DDI) USING MACHINE LEARNING NOVEL ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 2, page no.27-33, February-2020, Available :http://www.jetir.org/papers/JETIR2002407.pdf

"PREDICTION OF DRUG-DRUG INTERACTIONS (DDI) USING MACHINE LEARNING NOVEL ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 2, page no. pp27-33, February-2020, Available at : http://www.jetir.org/papers/JETIR2002407.pdf