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 Investigating  the Various Methods  for Predicting Drug–Drug Interactions Based on Machine Learning Model

M.Arunkumar

Research Scholar,

PG & Research Department of Computer Science,

A. Veeriya Vandayar Memorial Sri Pushpam College (Autonomous), Poondi - 613503, Thanjavur,

E-Mail: arunk145@gmail.com

Dr. T.S. Baskaran

Associate Professor& Research Supervisor,

PG & Research Department of Computer Science,

A Veeriya Vandayar Memorial Sri Pushpam College (Autonomous), Poondi - 613503, Thanjavur,

E-Mail: t_s_baskaran@yahoo.com

“Affiliated to Bharathidasan University, Tiruchirappalli-620024”, TamilNadu, India.

Abstract

Drug–drug interactions performance a vigorous role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first present the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions.

Keywords: machine learning, drug-drug interactions, comparation, prediction